From 8621c12a87195c49a563a3c3cc921af6eca52969 Mon Sep 17 00:00:00 2001 From: Micha Sam Brickman Raredon <42100128+msraredon@users.noreply.github.com> Date: Thu, 6 Aug 2026 20:05:21 -0400 Subject: [PATCH 01/13] feat: classic Visium (10x v1/v2) support MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Adds the platform with the largest published corpus in spatial transcriptomics, which was the one obvious gap: a Space Ranger `outs/` folder previously gave "Cannot detect spatial platform". Spots render as 55 um circle polygons on their hex lattice, with expression, both colour-value modes, the metadata filter, and NICHESv2 edges. Nothing renders cells() centroids, so synthesising polygons is what makes fill, colour-by, picking and region selection work — the same reasoning that made Visium HD bins squares. Circles rather than squares here because a bin really is square and a spot really is round. Shared base — readers/spaceranger.py Classic Visium and Visium HD agree on scalefactors, tissue_positions, the feature-matrix h5, the cells table, gene-set colouring and the sample-then-slice boundary logic. They differ in layout, unit shape and where the pixel size comes from. Only the differences stay in the two readers. Fixes an unapplied tissue_hires_scalef visium_hd_reader computed the factor and reported it in info() but never applied it to coordinates, while its own docstring said it did. Space Ranger reports positions in FULL-RESOLUTION pixels and ships only the hires PNG, which is what the tile pyramid is built from — so everything landed off the image by 1/scalef on any real dataset. It was invisible because visium_hd_tiny has the factor set to exactly 1.0. The factor is now applied in the shared positions loader and folded into pixel_size, which matters beyond distance labels: EdgeReader divides edges.parquet micron coordinates by pixel_size, so a fullres-based value would scatter the connectivity layer off the tissue. Behaviour on the bundled HD fixture is byte-identical (scalef is 1.0 there), which is precisely why the fixture could not catch it. Three classic-Visium traps, all handled - No microns_per_pixel key. 10x does not record image pixel size for classic Visium, so it is derived from the one guaranteed constant (55 um / spot_diameter_fullres) and flagged pixel_size_derived in info(), since 10x calls spot diameters estimates. - tissue_positions_list.csv (Space Ranger < 2.0) has no header row; default header inference eats the first spot and mislabels the rest. - tissue_hires_scalef really is ~0.08 here, not ~1.0. Fixture that can actually fail sample_data/make_visium.py generates a non-identity scalef of 0.08 on purpose. With the multiply, 252/252 spots land on the image; without it, 0/252. The derived pixel size recovers 0.25 um/px exactly, and niches_visium.R's independent lattice check reports a median nearest-neighbour distance of 100.0 um — the Visium pitch. Also - r/niches_visium.R, run end to end: 252 spots, 1642 edges, 30 LRMs, avg 6.5 neighbours per spot, which is the hex lattice's 6. - The gene-set hint no longer points at the Transcript Species section on platforms that hide it, and the colour-by mode is unit-labelled. - README platform table corrected: MERSCOPE and CosMx boundaries have been implemented since v0.7.1 but were still marked absent. Guard: 217 probes across 8 datasets, verified to reproduce without the gitignored metrics_summary.csv. Not yet verified against a real Space Ranger outs/ tree. Co-Authored-By: Claude Opus 5 --- CLAUDE.md | 108 ++- README.md | 23 +- backend/app/main.py | 2 +- backend/app/readers/reader_factory.py | 18 +- backend/app/readers/spaceranger.py | 398 +++++++++++ backend/app/readers/visium_hd_reader.py | 359 ++-------- backend/app/readers/visium_reader.py | 175 +++++ backend/tests/golden_baseline.json | 180 +++++ backend/tests/golden_snapshot.py | 1 + frontend/package.json | 2 +- frontend/src/components/LayerPanel.jsx | 14 +- r/README.md | 6 +- r/niches_visium.R | 187 ++++++ sample_data/make_visium.py | 252 +++++++ sample_data/visium_tiny/PROVENANCE.md | 48 ++ sample_data/visium_tiny/edges.parquet | Bin 0 -> 441907 bytes .../visium_tiny/filtered_feature_bc_matrix.h5 | Bin 0 -> 120020 bytes .../spatial/scalefactors_json.json | 6 + .../spatial/tissue_hires_image.png | Bin 0 -> 5904 bytes .../spatial/tissue_lowres_image.png | Bin 0 -> 1540 bytes .../visium_tiny/spatial/tissue_positions.csv | 617 ++++++++++++++++++ 21 files changed, 2061 insertions(+), 335 deletions(-) create mode 100644 backend/app/readers/spaceranger.py create mode 100644 backend/app/readers/visium_reader.py create mode 100644 r/niches_visium.R create mode 100644 sample_data/make_visium.py create mode 100644 sample_data/visium_tiny/PROVENANCE.md create mode 100644 sample_data/visium_tiny/edges.parquet create mode 100644 sample_data/visium_tiny/filtered_feature_bc_matrix.h5 create mode 100644 sample_data/visium_tiny/spatial/scalefactors_json.json create mode 100644 sample_data/visium_tiny/spatial/tissue_hires_image.png create mode 100644 sample_data/visium_tiny/spatial/tissue_lowres_image.png create mode 100644 sample_data/visium_tiny/spatial/tissue_positions.csv diff --git a/CLAUDE.md b/CLAUDE.md index e7e5b90..1b5589a 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -55,6 +55,7 @@ The backend uses an abstract reader pattern. All platform readers inherit from |---|---| | Xenium (10x Genomics) | `experiment.xenium` | | Visium HD (10x Genomics) | `binned_outputs/square_*um/` (or a top-level `square_???um/`) | +| Visium classic (10x Genomics) | `spatial/scalefactors_json.json` + a `tissue_positions*` file | | MERSCOPE (Vizgen) | `cell_by_gene.csv` or `cell_metadata.csv` | | CosMx (Nanostring) | `*_tx_file.csv` | | seqFISH (Spatial Genomics) | `*_CellCoordinates*.csv` — a glob, so registered **last** | @@ -73,6 +74,11 @@ and boundary layers rather than returning empty arrays for them. - Visium HD: implemented against a real Space Ranger 4.0.1 `outs/` tree — bins, bin outlines, expression, and both color-value modes. `has_transcripts: False` (bin-level UMI counts only, no molecule coordinates); `unit_label: "bin"`. See its own section. +- Visium classic (v1/v2): fully implemented — spots, spot outlines as circles, + expression, and both color-value modes. `has_transcripts: False`; + `unit_label: "spot"`. Shares `spaceranger.py` with Visium HD. **Verified only + against the synthetic fixture so far** — no real Space Ranger `outs/` tree has + been through it. See its own section. - MERSCOPE: cells, transcripts, genes, color-values (metadata + gene-set) implemented; `cell_boundaries()` returns empty and `has_boundaries: False` (HDF5 polygon format not yet parsed) @@ -116,6 +122,10 @@ backend/ metadata_filter.py Categorical-vs-continuous typing + the MetadataFilter subsetting spec; shared by cells and edges (#35, #45) spatial_cache.py Spatially-sorted parquet cache (build on first access) + spaceranger.py Shared base for the two 10x array platforms — scalefactors, + tissue_positions, the feature-matrix h5, and the hires + coordinate scaling. Read before touching either reader. + visium_reader.py Visium classic — spots as circle polygons; see its own section visium_hd_reader.py Visium HD — bins as square polygons; see its own section merscope_reader.py MERSCOPE implementation (inherits SpatialDatasetReader) cosmx_reader.py CosMx implementation (inherits SpatialDatasetReader) @@ -129,7 +139,7 @@ backend/ Dockerfile tests/ golden_snapshot.py Reader regression guard — see Development Workflow - golden_baseline.json Recorded baseline (191 probes / 7 datasets) + golden_baseline.json Recorded baseline (217 probes / 8 datasets) frontend/ src/ @@ -169,10 +179,14 @@ sample_data/ Partially gitignored — default data mount for loc and licence-restricted datasets are ignored. make_edges.py Synthetic edges.parquet generator make_seqfish.py Synthetic seqFISH v2 ROI generator (committable fixture) + make_visium.py Synthetic classic Visium generator (committable fixture); + non-identity tissue_hires_scalef on purpose — see below r/ NICHESv2 → edges.parquet. See r/README.md. niches_xenium.R Xenium — coordinates already µm; read this one first niches_seqfish.R seqFISH — dense CSV counts, per-version coordinate units niches_visium_hd.R Visium HD — pixel coordinates, must convert to µm + niches_visium.R Visium classic — same, but µm/px must be derived from the + 55 µm spot spec; checks it against the 100 µm pitch niches_common.R shared helpers (10x h5 reader, LR-coverage check, validation) *_PPLR.R older personal pipeline with hardcoded paths; reference only docs/ @@ -669,20 +683,102 @@ Visium HD reader would show an empty canvas. Emitting squares makes fill, outlin colour-by, picking and region selection all work through the existing layers with no frontend change at all. -**Coordinates.** `pxl_col/row_in_fullres` are already full-resolution image pixels, which -is exactly TissuePlex's contract, so they pass through untouched. `pixel_size` is -`microns_per_pixel` from the scalefactors and is used only to label distances. +**Coordinates.** See the shared section below — `pxl_col/row_in_fullres` are *full +resolution*, and the image TissuePlex renders is not. The bundled fixture **cannot catch a missing scalefactor multiply**: it has `tissue_hires_scalef = 1.0` and `microns_per_pixel = 1.003`, both effectively identity. Real datasets run ~0.02–0.2 and ~0.25. A passing render here is necessary, not sufficient — -see `sample_data/visium_hd_tiny/PROVENANCE.md`. +see `sample_data/visium_hd_tiny/PROVENANCE.md`. `sample_data/visium_tiny` was built with a +non-identity factor precisely to close that gap. Bin selection defaults to `square_008um` (Space Ranger's own analysis default, and `spatialdata-io`'s `DEFAULT_BIN`), falling back to the coarsest bin present. `info()` reports `bin` and `available_bins`; exposing bin choice in the UI would be the natural follow-up, in the shape of the edge-file picker. +Unexploited: `segmented_outputs/cell_segmentations.geojson`, which Space Ranger 4.x emits +and which would turn this from a bin viewer into a single-cell one. The seqFISH reader +already has GeoJSON ring-parsing to lift. + +--- + +## Visium classic (readers/visium_reader.py) + +The original Visium slide: 4,992 spots, each **55 µm across on a 100 µm hexagonal +pitch**, over a 6.5 × 6.5 mm capture area. One to ten cells per spot, so a spot is a +neighbourhood, not a cell — say so in figure legends, and note that NICHESv2 on this +platform scores spot–spot signalling. + +``` +dataset_dir/ ← point at the *contents* of Space Ranger's outs/ + filtered_feature_bc_matrix.h5 + spatial/ + tissue_positions.csv barcode, in_tissue, array_row, array_col, + pxl_row_in_fullres, pxl_col_in_fullres + scalefactors_json.json spot_diameter_fullres, tissue_hires_scalef, + tissue_lowres_scalef, fiducial_diameter_fullres + tissue_hires_image.png + tissue_lowres_image.png +``` + +Three things differ from Visium HD, and all three are traps: + +- **There is no `microns_per_pixel`.** Classic Visium scalefactors carry only those four + keys; 10x deliberately does not record image pixel size, noting that "prior knowledge + of the image pixel sizes is not used". `pixel_size` is therefore *derived* from the one + constant the slide guarantees — `55 / spot_diameter_fullres`. 10x cautions that spot + diameters are estimates and recommends a calibrated microscope value, so the derivation + is surfaced as `pixel_size_derived: true` in `info()` rather than passed off as + measured. Everything downstream inherits that uncertainty: the measurement tool, and + the placement of `edges.parquet` micron coordinates. +- **`tissue_positions_list.csv` (Space Ranger < 2.0) has no header row.** Read with + pandas' default header inference it eats the first spot and mislabels every column. + `spaceranger.py::_read_positions_table` names the columns explicitly for that filename. +- **`tissue_hires_scalef` is genuinely far from 1** — around 0.08. See below. + +**Spots are drawn as 16-gons, not squares.** An HD bin really is a square; a Visium spot +is round, and a square grid over a hex lattice misrepresents both the shape and the gaps +between spots. 16 vertices is affordable here in a way it would not be on HD: a full +capture area is 4,992 spots (~80K vertices) against HD's hundreds of thousands of bins. + +Detection requires *both* the scalefactors and a positions file, and is registered after +Visium HD. HD's top-level `spatial/` holds images only, so the two cannot currently +collide — ordering the more specific sentinel first means a future HD layout change +cannot silently reroute HD datasets here. + +--- + +## The Space Ranger coordinate contract (readers/spaceranger.py) + +Both 10x array platforms share a base class, because they agree on everything except +layout, unit shape and where the pixel size comes from. **Read this before touching +coordinates in either reader.** + +`pxl_col_in_fullres` / `pxl_row_in_fullres` are pixels in the **original +full-resolution microscope image**, which Space Ranger does not ship. What it ships is +`tissue_hires_image.png`, the same frame scaled by `tissue_hires_scalef`. TissuePlex +builds its tile pyramid from that PNG and derives its whole coordinate space from it, so +every coordinate the readers return is multiplied by that factor, and `pixel_size` reports +µm per **hires** pixel. + +This was wrong for a release. `hires_scalef` was computed and reported in `info()` but +never applied, while the module docstring claimed it was — invisible locally because the +HD fixture has the factor set to exactly 1.0. On real HD data it displaces everything by +1/scalef; on classic Visium, by about 12×. `sample_data/make_visium.py` therefore +generates a factor of 0.08, and the fixture check is decisive: 252/252 spots land on the +image with the multiply, 0/252 without it. + +Folding the factor into `pixel_size` matters beyond distance labels — `EdgeReader` divides +the micron coordinates in `edges.parquet` by `pixel_size` to place edges, so a +fullres-based value would scatter the connectivity layer off the tissue. + +**The caveat this leaves.** If a user drops their own full-resolution image into the +dataset folder and selects it, coordinates will be wrong by the same factor, because the +reader cannot know which image the viewer has open. Selecting `tissue_lowres_image` has +the same problem. Fixing it properly means per-image transforms, which is what +`spatialdata` does and what TissuePlex's single global image space does not model. + --- ## seqFISH / Spatial Genomics (readers/seqfish_reader.py) @@ -1026,7 +1122,7 @@ auth, no user accounts, and no per-dataset permissions. **Regression guard.** `backend/tests/golden_snapshot.py` exercises every reader method against all local datasets, digests the results, and diffs them against a recorded -baseline (100 probes across 4 datasets). Run it after any reader change: +baseline (217 probes across 8 datasets). Run it after any reader change: ```bash cd backend && python3 tests/golden_snapshot.py # check diff --git a/README.md b/README.md index 205941a..892f9fd 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ An interactive spatial transcriptomics viewer for exploring cell-cell communicat ## What it does -Spatial transcriptomics platforms (Xenium, seqFISH, Visium HD, MERSCOPE, CosMx) produce high-resolution images with hundreds of genes measured per cell. NICHESv2 infers which cells are communicating and through which ligand-receptor mechanisms (LRMs). TissuePlex bridges those two outputs: it overlays the NICHESv2 communication graph on the tissue image and lets you explore it interactively. +Spatial transcriptomics platforms (Xenium, seqFISH, Visium, Visium HD, MERSCOPE, CosMx) produce high-resolution images with hundreds of genes measured per cell. NICHESv2 infers which cells are communicating and through which ligand-receptor mechanisms (LRMs). TissuePlex bridges those two outputs: it overlays the NICHESv2 communication graph on the tissue image and lets you explore it interactively. **Key capabilities:** @@ -39,12 +39,13 @@ Spatial transcriptomics platforms (Xenium, seqFISH, Visium HD, MERSCOPE, CosMx) | **Xenium** | 10x Genomics | ✓ | ✓ | ✓ | ✓ | | **seqFISH** | Spatial Genomics | ✓ | ✓ | ✓ | ✓ | | **Visium HD** | 10x Genomics | ✓ | — | ✓ (bins) | ✓ | -| **MERSCOPE** | Vizgen | — | ✓ | — | ✓ | -| **CosMx** | Nanostring | — | ✓ | — | ✓ | +| **Visium** | 10x Genomics | ✓ | — | ✓ (spots) | ✓ | +| **MERSCOPE** | Vizgen | ✓ | ✓ | ✓ | ✓ | +| **CosMx** | Nanostring | placeholder | ✓ | ✓ | ✓ | -Xenium and seqFISH are the complete implementations. seqFISH means the commercial **Spatial Genomics GenePS** output, not the academic seqFISH/seqFISH+ method, which has no standard file layout; the current v2 layout is fully supported, and legacy v1 reads cells and transcripts but not boundaries. +seqFISH means the commercial **Spatial Genomics GenePS** output, not the academic seqFISH/seqFISH+ method, which has no standard file layout; the current v2 layout is fully supported, and legacy v1 reads cells and transcripts but not boundaries. -The other readers cover cells, transcripts, and metadata coloring; boundary parsing is platform-specific and not yet implemented for them (MERSCOPE stores polygons in HDF5, CosMx in per-FOV label TIFFs). Visium HD renders each square bin as a polygon — so fill, colour-by, picking and region selection all work — but has no per-molecule transcript coordinates. Each reader declares what it supports via a capability flag, and the UI hides layers the platform cannot serve. +The two array-based platforms have no per-molecule transcript coordinates — only spot- or bin-level UMI counts — so they synthesise a polygon per unit instead: a square for a Visium HD bin, a circle for a 55 µm Visium spot. Fill, colour-by, picking and region selection then all work through the same layers. CosMx datasets often ship no morphology image, in which case TissuePlex renders the data onto a blank canvas sized to the tissue. Each reader declares what it supports via a capability flag, and the UI hides layers the platform cannot serve — and relabels itself, so a Visium dataset says "spot" wherever a Xenium one says "cell". The edge connectivity layer (NICHESv2 output) works with any platform — it is platform-agnostic as long as cell barcodes match. @@ -101,13 +102,21 @@ DATA_PATH=/absolute/path/to/your/datasets docker compose up --build spatial/{tissue_positions.parquet, scalefactors_json.json, tissue_hires_image.png} edges.parquet - merscope_run_D/ + visium_run_D/ + filtered_feature_bc_matrix.h5 + spatial/ ← Visium sentinel: scalefactors_json.json + + scalefactors_json.json tissue_positions.csv + tissue_positions.csv + tissue_hires_image.png + edges.parquet + + merscope_run_E/ cell_by_gene.csv ← MERSCOPE sentinel cell_metadata.csv detected_transcripts.csv edges.parquet - cosmx_run_E/ + cosmx_run_F/ my_experiment_tx_file.csv ← CosMx sentinel edges.parquet ``` diff --git a/backend/app/main.py b/backend/app/main.py index b393894..701e166 100644 --- a/backend/app/main.py +++ b/backend/app/main.py @@ -3,7 +3,7 @@ from app.routers import tiles, spatial, edges, layers -APP_VERSION = "0.8.0" +APP_VERSION = "0.9.0" app = FastAPI(title="TissuePlex API", version=APP_VERSION) diff --git a/backend/app/readers/reader_factory.py b/backend/app/readers/reader_factory.py index 3c48e87..4c4f11e 100644 --- a/backend/app/readers/reader_factory.py +++ b/backend/app/readers/reader_factory.py @@ -8,6 +8,7 @@ Supported platforms (detection order): Xenium (10x Genomics) — experiment.xenium Visium HD (10x Genomics) — square_???um/ subdirectory + Visium (10x Genomics) — spatial/scalefactors_json.json + tissue_positions* MERSCOPE (Vizgen) — cell_by_gene.csv or cell_metadata.csv CosMx (Nanostring) — *_tx_file.csv seqFISH (Spatial Genomics) — *_CellCoordinates*.csv (glob; registered last) @@ -46,6 +47,15 @@ def _is_visium_hd(path: Path) -> bool: or any(path.glob("square_???um"))) +def _is_visium(path: Path) -> bool: + # Classic Visium. Registered *after* Visium HD: HD also has a top-level + # spatial/ folder, and although that one holds images only (no scalefactors), + # ordering the more specific sentinel first means a future HD layout change + # cannot silently reroute HD datasets to this reader. + from app.readers.visium_reader import VisiumReader + return VisiumReader.looks_like_visium(path) + + def _is_merscope(path: Path) -> bool: return ( (path / "cell_by_gene.csv").exists() or @@ -88,6 +98,11 @@ def _make_visium_hd(path: Path) -> SpatialDatasetReader: return VisiumHDReader(path) +def _make_visium(path: Path) -> SpatialDatasetReader: + from app.readers.visium_reader import VisiumReader + return VisiumReader(path) + + def _make_merscope(path: Path) -> SpatialDatasetReader: from app.readers.merscope_reader import MerscopeReader return MerscopeReader(path) @@ -105,6 +120,7 @@ def _make_seqfish(path: Path) -> SpatialDatasetReader: _register(_is_xenium, _make_xenium, "experiment.xenium (Xenium / 10x)") _register(_is_visium_hd, _make_visium_hd, "square_???um/ directory (Visium HD / 10x)") +_register(_is_visium, _make_visium, "spatial/scalefactors_json.json + tissue_positions (Visium / 10x)") _register(_is_merscope, _make_merscope, "cell_by_gene.csv or cell_metadata.csv (MERSCOPE / Vizgen)") _register(_is_cosmx, _make_cosmx, "*_tx_file.csv (CosMx / Nanostring)") _register(_is_seqfish, _make_seqfish, "*_CellCoordinates*.csv (seqFISH / Spatial Genomics)") @@ -133,4 +149,4 @@ def is_dataset(path: Path) -> bool: @staticmethod def supported_platforms() -> list[str]: """Names of all registered platforms, in detection-priority order.""" - return ["xenium", "visium_hd", "merscope", "cosmx", "seqfish"] + return ["xenium", "visium_hd", "visium", "merscope", "cosmx", "seqfish"] diff --git a/backend/app/readers/spaceranger.py b/backend/app/readers/spaceranger.py new file mode 100644 index 0000000..fcb15f8 --- /dev/null +++ b/backend/app/readers/spaceranger.py @@ -0,0 +1,398 @@ +""" +Shared base for 10x Space Ranger output — classic Visium and Visium HD. + +The two platforms differ in three things and agree on everything else: + +============ ========================== ============================== + classic Visium Visium HD +============ ========================== ============================== +layout ``spatial/`` at the root ``binned_outputs/square_*um/`` +unit shape 55 µm circle square of ``bin_size_um`` +pixel size derived from the spot spec ``microns_per_pixel`` key +============ ========================== ============================== + +Everything else — scalefactors, ``tissue_positions``, the 10x feature-matrix +HDF5, the cells table, gene-set colouring, and the sample-then-slice boundary +logic — is identical, so it lives here. Subclasses supply the three differences +through ``_spatial_dir()``, ``_matrix_path()``, ``_unit_diameter_fullres()`` and +``_unit_offsets()``. + +Coordinate space: the hires image, not full resolution +------------------------------------------------------ +``pxl_col_in_fullres`` / ``pxl_row_in_fullres`` are pixels in the **original +full-resolution microscope image**, which Space Ranger does not ship. What it +ships is ``tissue_hires_image.png``, the same frame scaled by +``tissue_hires_scalef``. TissuePlex builds its tile pyramid from that PNG and +derives its entire coordinate space from it, so every coordinate this reader +returns is multiplied by that factor. + +Skip the multiply and the data lands off the image by 1/scalef — about 12× on a +typical classic Visium dataset, where the factor runs ~0.08. It is easy to skip +because the bundled Visium HD fixture has ``tissue_hires_scalef: 1.0``, so the +bug is invisible there; ``sample_data/make_visium.py`` deliberately generates a +non-identity factor for exactly this reason. + +``pixel_size`` therefore reports µm per **hires** pixel, not per fullres pixel. +That matters beyond distance labels: ``EdgeReader`` divides the micron +coordinates in ``edges.parquet`` by ``pixel_size`` to place edges, so a +fullres-based value would scatter the connectivity layer off the tissue. + +The caveat this leaves: if a user drops their own full-resolution image into the +dataset folder and selects it, coordinates will be wrong by the same factor, +because the reader has no way to know which image the viewer has open. Selecting +``tissue_lowres_image`` has the same problem. Both are recorded in the platform +docs rather than guessed at here. +""" +import json +from pathlib import Path +from typing import Optional + +import numpy as np +import pandas as pd + +from app.readers.base_reader import _UNSET, SpatialDatasetReader + + +class SpaceRangerReader(SpatialDatasetReader): + + # Space Ranger's own root CSVs, so the shared supplemental-metadata loader + # never ingests them as user annotations. + _ROOT_CSV_SKIP = frozenset({"metrics_summary.csv"}) + + # Prefix used in log lines; subclasses override. + _LOG = "spaceranger" + + def __init__(self, dataset_path: Path): + super().__init__(dataset_path) + self._scalefactors_cache = _UNSET + self._positions_cache = _UNSET + self._cells_full_cache = _UNSET + self._matrix_cache = _UNSET + + # ── Subclass hooks ──────────────────────────────────────────────────────── + + def _spatial_dir(self) -> Optional[Path]: + """Directory holding scalefactors_json.json and tissue_positions.*.""" + raise NotImplementedError + + def _matrix_path(self) -> Optional[Path]: + """Path to filtered_feature_bc_matrix.h5, or None.""" + raise NotImplementedError + + def _unit_diameter_fullres(self) -> float: + """Width of one unit in **full-resolution** pixels.""" + raise NotImplementedError + + @staticmethod + def _unit_offsets(radius: float) -> tuple: + """Vertex offsets from a unit's centre, in image pixels. + + deck.gl closes the ring itself, so the first vertex must not be repeated + — matching every other reader. + """ + raise NotImplementedError + + def _microns_per_fullres_px(self) -> float: + """µm per full-resolution pixel. Scaled to hires by `pixel_size`.""" + raise NotImplementedError + + # ── Scalefactors ────────────────────────────────────────────────────────── + + def _scalefactors(self) -> dict: + if self._scalefactors_cache is not _UNSET: + return self._scalefactors_cache # type: ignore[return-value] + self._scalefactors_cache = {} + spatial = self._spatial_dir() + if spatial is not None: + f = spatial / "scalefactors_json.json" + if f.exists(): + try: + self._scalefactors_cache = json.loads(f.read_text()) + except Exception as exc: + print(f"[{self._LOG}] could not read {f.name}: {exc}") + return self._scalefactors_cache # type: ignore[return-value] + + @property + def hires_scalef(self) -> float: + """Factor mapping full-resolution pixels onto tissue_hires_image.png.""" + try: + v = float(self._scalefactors().get("tissue_hires_scalef", 1.0)) + return v if v > 0 else 1.0 + except (TypeError, ValueError): + return 1.0 + + @property + def pixel_size(self) -> float: + """µm per **hires** image pixel — the space this reader returns.""" + try: + v = self._microns_per_fullres_px() / self.hires_scalef + return v if v > 0 and np.isfinite(v) else 1.0 + except (TypeError, ValueError, ZeroDivisionError): + return 1.0 + + def unit_diameter_px(self) -> float: + """Width of one unit in image (hires) pixels.""" + return self._unit_diameter_fullres() * self.hires_scalef + + # ── Positions ───────────────────────────────────────────────────────────── + + def _read_positions_table(self) -> Optional[pd.DataFrame]: + """Raw tissue_positions in whichever of the three forms shipped. + + Space Ranger < 2.0 wrote ``tissue_positions_list.csv`` with **no header + row**; 2.0 renamed it and added one. Reading the old file with the + default header inference silently eats the first spot and mislabels + every column after it, so the legacy name is read with explicit names. + """ + spatial = self._spatial_dir() + if spatial is None: + return None + cols = ["barcode", "in_tissue", "array_row", "array_col", + "pxl_row_in_fullres", "pxl_col_in_fullres"] + try: + pq = spatial / "tissue_positions.parquet" + if pq.exists(): + return pd.read_parquet(pq) + csv = spatial / "tissue_positions.csv" + if csv.exists(): + return pd.read_csv(csv) + legacy = spatial / "tissue_positions_list.csv" + if legacy.exists(): + return pd.read_csv(legacy, header=None, names=cols) + except Exception as exc: + print(f"[{self._LOG}] could not read tissue positions: {exc}") + return None + + def _positions(self) -> pd.DataFrame: + """In-tissue units with centroids in hires-image pixels, one row each.""" + if self._positions_cache is not _UNSET: + return self._positions_cache # type: ignore[return-value] + self._positions_cache = pd.DataFrame() + + df = self._read_positions_table() + if df is None: + return self._positions_cache # type: ignore[return-value] + if not {"pxl_col_in_fullres", "pxl_row_in_fullres"} <= set(df.columns): + print(f"[{self._LOG}] tissue positions lack pxl_col/row_in_fullres") + return self._positions_cache # type: ignore[return-value] + + # Most units fall outside the tissue; some carry negative pixel + # coordinates because the capture area is larger than the image. + if "in_tissue" in df.columns: + df = df[df["in_tissue"] == 1] + + scale = self.hires_scalef + out = pd.DataFrame({ + "cell_id": df["barcode"].astype(str) if "barcode" in df.columns + else df.index.astype(str), + "x_centroid": df["pxl_col_in_fullres"].astype(float) * scale, + "y_centroid": df["pxl_row_in_fullres"].astype(float) * scale, + }) + for extra in ("array_row", "array_col"): + if extra in df.columns: + out[extra] = df[extra].to_numpy() + out = out.reset_index(drop=True) + self._positions_cache = out + return out + + def _cells_full(self) -> Optional[pd.DataFrame]: + if self._cells_full_cache is not _UNSET: + return self._cells_full_cache # type: ignore[return-value] + pos = self._positions() + self._cells_full_cache = self._merge_supplemental( + None if pos.empty else pos + ) + return self._cells_full_cache # type: ignore[return-value] + + def _metadata_frame(self): + return self._cells_full() + + # ── Cells (spots / bins) ────────────────────────────────────────────────── + + def cells(self, bbox: Optional[tuple] = None) -> list[dict]: + df = self._cells_full() + if df is None or df.empty: + return [] + if bbox: + xmin, ymin, xmax, ymax = bbox + if None not in (xmin, ymin, xmax, ymax): + df = df[(df["x_centroid"] >= xmin) & (df["x_centroid"] <= xmax) & + (df["y_centroid"] >= ymin) & (df["y_centroid"] <= ymax)] + return self._to_records(df) + + def cells_schema(self) -> dict: + df = self._cells_full() + if df is None or df.empty: + return {"columns": {}} + return {"columns": {c: str(df[c].dtype) for c in df.columns if c != "cell_id"}} + + def cell_detail(self, cell_id: str) -> Optional[dict]: + df = self._cells_full() + if df is None or df.empty: + return None + row = df[df["cell_id"] == str(cell_id)] + if row.empty: + return None + rec = self._to_records(row)[0] + rec["expression"] = self.cell_expression(cell_id) + return rec + + # ── Boundaries ──────────────────────────────────────────────────────────── + + def cell_boundaries(self, bbox: Optional[tuple] = None, + fraction: float = 1.0, + cell_ids: Optional[set] = None) -> dict: + """Unit outlines as polygons, in image pixel space. + + A unit qualifies if its centroid lies in the bbox, and then all of its + vertices are returned — the whole-unit rule every reader follows, so + units are never clipped into partial shapes at the viewport edge. + + Nothing in the frontend renders `cells()` centroids; the boundary layers + are the only path to drawing a unit. That is why both spot-based + platforms synthesise polygons instead of declaring `has_boundaries: + False`, which would leave the canvas empty. + """ + df = self._cells_full() + if df is None or df.empty: + return {"boundaries": [], "total": 0} + + # Metadata filter (issue #45), before the bbox count and the sample. + if cell_ids is not None: + df = df[df["cell_id"].astype(str).isin(cell_ids)] + + if bbox: + xmin, ymin, xmax, ymax = bbox + if None not in (xmin, ymin, xmax, ymax): + df = df[(df["x_centroid"] >= xmin) & (df["x_centroid"] <= xmax) & + (df["y_centroid"] >= ymin) & (df["y_centroid"] <= ymax)] + total = len(df) + if total == 0: + return {"boundaries": [], "total": 0} + + fraction = max(0.0001, min(1.0, fraction)) + n = round(fraction * total) + if n <= 0: + return {"boundaries": [], "total": total} + if n < total: + # Deterministic, evenly spread subset — re-fetching an unchanged + # viewport must return the same units or the layer flickers. + df = df.iloc[np.linspace(0, total - 1, n).astype(int)] + + offsets = self._unit_offsets(self.unit_diameter_px() / 2.0) + ids = df["cell_id"].to_numpy() + xs = df["x_centroid"].to_numpy() + ys = df["y_centroid"].to_numpy() + rows = [ + {"cell_id": ids[i], "vertex_x": float(xs[i] + dx), "vertex_y": float(ys[i] + dy)} + for i in range(len(ids)) for dx, dy in offsets + ] + return {"boundaries": rows, "total": total} + + # ── No per-molecule transcripts on either platform ──────────────────────── + + def transcripts(self, bbox: Optional[tuple] = None, + genes: Optional[list[str]] = None, + fraction: float = 1.0) -> dict: + """Neither Visium nor Visium HD has molecule-level detections. + + Returns the same dict shape as every other reader rather than a bare + list, so a caller that ignores `has_transcripts` gets an empty result + instead of a TypeError. + """ + return {"transcripts": [], "total": 0} + + # ── Expression (filtered_feature_bc_matrix.h5) ──────────────────────────── + + def _matrix(self): + """(barcodes, gene_names, csc_matrix), or None. + + The same 10x HDF5 layout Xenium uses for cell_feature_matrix.h5. + """ + if self._matrix_cache is not _UNSET: + return self._matrix_cache + self._matrix_cache = None + h5 = self._matrix_path() + if h5 is None or not h5.exists(): + return None + try: + import h5py + import scipy.sparse as sp + with h5py.File(h5, "r") as f: + barcodes = f["matrix/barcodes"][()].astype(str).tolist() + names = f["matrix/features/name"][()].astype(str).tolist() + mat = sp.csc_matrix( + (f["matrix/data"][()], f["matrix/indices"][()], f["matrix/indptr"][()]), + shape=(len(names), len(barcodes)), + ) + self._matrix_cache = (barcodes, names, mat) + except Exception as exc: + print(f"[{self._LOG}] could not read {h5.name}: {exc}") + return self._matrix_cache + + def gene_list(self) -> list[str]: + m = self._matrix() + if m is None: + return [] + _, names, _ = m + # Deduplicate while preserving order — 10x references repeat a symbol. + seen, out = set(), [] + for g in names: + if g not in seen: + seen.add(g) + out.append(g) + return out + + def cell_expression(self, cell_id: str) -> dict: + m = self._matrix() + if m is None: + return {} + barcodes, names, mat = m + try: + idx = barcodes.index(str(cell_id)) + except ValueError: + return {} + col = mat.getcol(idx).toarray().ravel() + nz = np.nonzero(col)[0] + return {names[i]: int(col[i]) for i in nz} + + # ── Colour values ───────────────────────────────────────────────────────── + + def color_values(self, mode: str, field: Optional[str] = None, + genes: Optional[list[str]] = None, + categorical: Optional[bool] = None) -> dict: + if mode == "gene_set": + return self._color_values_gene_set(genes or []) + return self._color_values_meta(field or "", categorical) + + def _color_values_gene_set(self, genes: list[str]) -> dict: + empty = {"type": "continuous", "values": {}, "min": 0.0, "max": 0.0} + m = self._matrix() + if m is None or not genes: + return empty + barcodes, names, mat = m + wanted = set(genes) + rows = [i for i, g in enumerate(names) if g in wanted] + if not rows: + return empty + summed = np.asarray(mat[rows, :].sum(axis=0)).ravel() + vmax = float(summed.max()) if summed.size and summed.max() > 0 else 1.0 + return { + "type": "continuous", + "values": {barcodes[i]: float(summed[i]) for i in range(len(barcodes))}, + "min": 0.0, + "max": vmax, + } + + # ── Morphology ──────────────────────────────────────────────────────────── + + _MORPHOLOGY_NAMES = ("tissue_hires_image.png", "tissue_lowres_image.png") + + def _morphology_exists(self) -> bool: + spatial = self._spatial_dir() + for base in (spatial, self.path / "spatial"): + if base is None: + continue + if any((base / n).exists() for n in self._MORPHOLOGY_NAMES): + return True + return False diff --git a/backend/app/readers/visium_hd_reader.py b/backend/app/readers/visium_hd_reader.py index 9c57b4f..c728766 100644 --- a/backend/app/readers/visium_hd_reader.py +++ b/backend/app/readers/visium_hd_reader.py @@ -29,33 +29,23 @@ ``cell_boundaries()`` emits four vertices per bin rather than reporting ``has_boundaries: False``. That is an honest representation of the data, and it means fill, outline, colour-by, picking and region selection all work through the -existing layers with no frontend change. Nothing renders `cells()` centroids — -boundaries are the only path to drawing a unit — so declaring no boundaries would -leave the canvas empty. +existing layers with no frontend change. -Coordinates ------------ -``pxl_col_in_fullres`` / ``pxl_row_in_fullres`` are already in **full-resolution -image pixels**, which is exactly TissuePlex's contract, so they pass through -untouched. ``pixel_size`` is ``microns_per_pixel`` from the scalefactors, and is -used only to label distances in µm. +Everything not specific to bins — scalefactors, positions, the feature matrix, +the coordinate scaling, colour values — lives in ``spaceranger.py``, shared with +the classic Visium reader. **Read that module's docstring before touching +coordinates here**; in particular ``tissue_hires_scalef`` is applied there, and +the bundled fixture has it set to 1.0 so a regression would be invisible locally. -**The morphology image is not always full-resolution.** ``tissue_hires_image.png`` -is the fullres frame scaled by ``tissue_hires_scalef``, so bin coordinates must be -multiplied by that factor to land on it. On the bundled tiny dataset the factor is -exactly 1.0 and ``microns_per_pixel`` is 1.003 — both effectively identity — so a -missing multiply would look perfectly correct there and break on every real -dataset, where the factor runs ~0.02–0.2. The multiply is therefore explicit and -covered by an assertion in the reader tests rather than left implicit. +Not yet used: ``segmented_outputs/cell_segmentations.geojson``. Space Ranger 4.x +emits real per-cell polygons, which would turn this from a bin viewer into a +single-cell one. The seqFISH reader already has GeoJSON ring-parsing to lift. """ -import json from pathlib import Path from typing import Optional -import numpy as np -import pandas as pd - -from app.readers.base_reader import _UNSET, SpatialDatasetReader +from app.readers.base_reader import _UNSET +from app.readers.spaceranger import SpaceRangerReader # Space Ranger's own analysis default, and spatialdata-io's DEFAULT_BIN. # Preferred when present; otherwise the coarsest bin available, since coarser @@ -65,19 +55,13 @@ _FALLBACK_MICRONS_PER_PIXEL = 1.0 -class VisiumHDReader(SpatialDatasetReader): +class VisiumHDReader(SpaceRangerReader): - # Space Ranger's own root CSVs, so the shared supplemental-metadata loader - # never ingests them as user annotations. - _ROOT_CSV_SKIP = frozenset({"metrics_summary.csv"}) + _LOG = "visium_hd" def __init__(self, dataset_path: Path): super().__init__(dataset_path) self._bin_dir_cache = _UNSET - self._scalefactors_cache = _UNSET - self._positions_cache = _UNSET - self._cells_full_cache = _UNSET - self._matrix_cache = _UNSET # ── Layout ──────────────────────────────────────────────────────────────── @@ -106,62 +90,55 @@ def _bin_dir(self) -> Optional[Path]: self._bin_dir_cache = chosen return chosen - def _scalefactors(self) -> dict: - if self._scalefactors_cache is not _UNSET: - return self._scalefactors_cache # type: ignore[return-value] - self._scalefactors_cache = {} - bin_dir = self._bin_dir() - if bin_dir is not None: - f = bin_dir / "spatial" / "scalefactors_json.json" - if f.exists(): - try: - self._scalefactors_cache = json.loads(f.read_text()) - except Exception as exc: - print(f"[visium_hd] could not read {f.name}: {exc}") - return self._scalefactors_cache # type: ignore[return-value] + # ── Space Ranger hooks ──────────────────────────────────────────────────── - # ── Identity ────────────────────────────────────────────────────────────── + def _spatial_dir(self) -> Optional[Path]: + bin_dir = self._bin_dir() + return None if bin_dir is None else bin_dir / "spatial" - @property - def platform(self) -> str: - return "visium_hd" + def _matrix_path(self) -> Optional[Path]: + bin_dir = self._bin_dir() + return None if bin_dir is None else bin_dir / "filtered_feature_bc_matrix.h5" - @property - def pixel_size(self) -> float: - """µm per full-resolution image pixel.""" - v = self._scalefactors().get("microns_per_pixel") + def _microns_per_fullres_px(self) -> float: + """Visium HD states this outright, unlike classic Visium.""" try: - v = float(v) + v = float(self._scalefactors().get("microns_per_pixel")) return v if v > 0 else _FALLBACK_MICRONS_PER_PIXEL except (TypeError, ValueError): return _FALLBACK_MICRONS_PER_PIXEL - @property - def hires_scalef(self) -> float: - """Factor mapping full-resolution pixels onto tissue_hires_image.png.""" - try: - return float(self._scalefactors().get("tissue_hires_scalef", 1.0)) - except (TypeError, ValueError): - return 1.0 - - def _bin_side_px(self) -> float: + def _unit_diameter_fullres(self) -> float: """Bin square side length, in full-resolution pixels.""" sf = self._scalefactors() - for key in ("spot_diameter_fullres",): - try: - v = float(sf[key]) - if v > 0: - return v - except (KeyError, TypeError, ValueError): - pass + try: + v = float(sf["spot_diameter_fullres"]) + if v > 0: + return v + except (KeyError, TypeError, ValueError): + pass # Derive from the bin size in µm if the diameter is missing. try: - return float(sf["bin_size_um"]) / self.pixel_size + return float(sf["bin_size_um"]) / self._microns_per_fullres_px() except (KeyError, TypeError, ValueError, ZeroDivisionError): return 1.0 + @staticmethod + def _unit_offsets(radius: float) -> tuple: + """A bin is a square. Corners counter-clockwise, ring left open.""" + return ((-radius, -radius), (radius, -radius), + (radius, radius), (-radius, radius)) + # ── Experiment metadata ─────────────────────────────────────────────────── + def _bin_side_px(self) -> float: + """Bin side length in image pixels. Kept as the platform-specific name.""" + return self.unit_diameter_px() + + @property + def platform(self) -> str: + return "visium_hd" + def info(self) -> dict: bin_dir = self._bin_dir() sf = self._scalefactors() @@ -184,249 +161,3 @@ def capabilities(self) -> dict: "has_boundaries": self._bin_dir() is not None, "unit_label": "bin", } - - def _morphology_exists(self) -> bool: - bin_dir = self._bin_dir() - candidates = [] - if bin_dir is not None: - candidates.append(bin_dir / "spatial" / "tissue_hires_image.png") - candidates.append(self.path / "spatial" / "tissue_hires_image.png") - return any(c.exists() for c in candidates) - - # ── Positions ───────────────────────────────────────────────────────────── - - def _positions(self) -> pd.DataFrame: - """In-tissue bins with pixel-space centroids, indexed by barcode.""" - if self._positions_cache is not _UNSET: - return self._positions_cache # type: ignore[return-value] - self._positions_cache = pd.DataFrame() - bin_dir = self._bin_dir() - if bin_dir is None: - return self._positions_cache # type: ignore[return-value] - - spatial = bin_dir / "spatial" - pos = spatial / "tissue_positions.parquet" - try: - if pos.exists(): - df = pd.read_parquet(pos) - else: - csv = spatial / "tissue_positions.csv" - if not csv.exists(): - return self._positions_cache # type: ignore[return-value] - df = pd.read_csv(csv) - except Exception as exc: - print(f"[visium_hd] could not read tissue positions: {exc}") - return self._positions_cache # type: ignore[return-value] - - if not {"pxl_col_in_fullres", "pxl_row_in_fullres"} <= set(df.columns): - print("[visium_hd] tissue positions lack pxl_col/row_in_fullres") - return self._positions_cache # type: ignore[return-value] - - # Most bins fall outside the tissue; some even carry negative pixel - # coordinates because the capture area is larger than the image. - if "in_tissue" in df.columns: - df = df[df["in_tissue"] == 1] - - out = pd.DataFrame({ - "cell_id": df["barcode"].astype(str) if "barcode" in df.columns - else df.index.astype(str), - "x_centroid": df["pxl_col_in_fullres"].astype(float), - "y_centroid": df["pxl_row_in_fullres"].astype(float), - }) - for extra in ("array_row", "array_col"): - if extra in df.columns: - out[extra] = df[extra].to_numpy() - out = out.reset_index(drop=True) - self._positions_cache = out - return out - - def _cells_full(self) -> Optional[pd.DataFrame]: - if self._cells_full_cache is not _UNSET: - return self._cells_full_cache # type: ignore[return-value] - pos = self._positions() - self._cells_full_cache = self._merge_supplemental( - None if pos.empty else pos - ) - return self._cells_full_cache # type: ignore[return-value] - - # ── Cells (bins) ────────────────────────────────────────────────────────── - - def cells(self, bbox: Optional[tuple] = None) -> list[dict]: - df = self._cells_full() - if df is None or df.empty: - return [] - if bbox: - xmin, ymin, xmax, ymax = bbox - if None not in (xmin, ymin, xmax, ymax): - df = df[(df["x_centroid"] >= xmin) & (df["x_centroid"] <= xmax) & - (df["y_centroid"] >= ymin) & (df["y_centroid"] <= ymax)] - return self._to_records(df) - - def cells_schema(self) -> dict: - df = self._cells_full() - if df is None or df.empty: - return {"columns": {}} - return {"columns": {c: str(df[c].dtype) for c in df.columns if c != "cell_id"}} - - def cell_detail(self, cell_id: str) -> Optional[dict]: - df = self._cells_full() - if df is None or df.empty: - return None - row = df[df["cell_id"] == str(cell_id)] - if row.empty: - return None - rec = self._to_records(row)[0] - rec["expression"] = self.cell_expression(cell_id) - return rec - - # ── Boundaries: each bin as a square ────────────────────────────────────── - - def cell_boundaries(self, bbox: Optional[tuple] = None, - fraction: float = 1.0, - cell_ids: Optional[set] = None) -> dict: - """Bin outlines as square polygons, in pixel space. - - A bin qualifies if its centroid lies in the bbox, and then all four of its - corners are returned — the same whole-unit rule the other readers use, so - bins are never clipped into partial shapes at the viewport edge. - """ - df = self._cells_full() - if df is None or df.empty: - return {"boundaries": [], "total": 0} - - # Metadata filter (issue #45), before the bbox count and the sample. - if cell_ids is not None: - df = df[df["cell_id"].astype(str).isin(cell_ids)] - - if bbox: - xmin, ymin, xmax, ymax = bbox - if None not in (xmin, ymin, xmax, ymax): - df = df[(df["x_centroid"] >= xmin) & (df["x_centroid"] <= xmax) & - (df["y_centroid"] >= ymin) & (df["y_centroid"] <= ymax)] - total = len(df) - if total == 0: - return {"boundaries": [], "total": 0} - - fraction = max(0.0001, min(1.0, fraction)) - n = round(fraction * total) - if n <= 0: - return {"boundaries": [], "total": total} - if n < total: - # Deterministic, evenly spread subset — re-fetching an unchanged - # viewport must return the same bins or the layer flickers. - df = df.iloc[np.linspace(0, total - 1, n).astype(int)] - - half = self._bin_side_px() / 2.0 - ids = df["cell_id"].to_numpy() - xs = df["x_centroid"].to_numpy() - ys = df["y_centroid"].to_numpy() - # Corners counter-clockwise; deck.gl closes the ring itself, so the first - # vertex is not repeated (matching the other readers). - offsets = ((-half, -half), (half, -half), (half, half), (-half, half)) - rows = [ - {"cell_id": ids[i], "vertex_x": float(xs[i] + dx), "vertex_y": float(ys[i] + dy)} - for i in range(len(ids)) for dx, dy in offsets - ] - return {"boundaries": rows, "total": total} - - # ── No per-molecule transcripts ─────────────────────────────────────────── - - def transcripts(self, bbox: Optional[tuple] = None, - genes: Optional[list[str]] = None, - fraction: float = 1.0) -> dict: - """Visium HD has no molecule-level detections. - - Returns the same dict shape as every other reader rather than a bare list, - so a caller that ignores `has_transcripts` gets an empty result instead of - a TypeError. - """ - return {"transcripts": [], "total": 0} - - # ── Expression (filtered_feature_bc_matrix.h5) ──────────────────────────── - - def _matrix(self): - """(barcodes, gene_names, csc_matrix) from the bin's feature matrix, or None. - - Same 10x HDF5 layout Xenium uses for cell_feature_matrix.h5. - """ - if self._matrix_cache is not _UNSET: - return self._matrix_cache - self._matrix_cache = None - bin_dir = self._bin_dir() - if bin_dir is None: - return None - h5 = bin_dir / "filtered_feature_bc_matrix.h5" - if not h5.exists(): - return None - try: - import h5py - import scipy.sparse as sp - with h5py.File(h5, "r") as f: - barcodes = f["matrix/barcodes"][()].astype(str).tolist() - names = f["matrix/features/name"][()].astype(str).tolist() - mat = sp.csc_matrix( - (f["matrix/data"][()], f["matrix/indices"][()], f["matrix/indptr"][()]), - shape=(len(names), len(barcodes)), - ) - self._matrix_cache = (barcodes, names, mat) - except Exception as exc: - print(f"[visium_hd] could not read {h5.name}: {exc}") - return self._matrix_cache - - def gene_list(self) -> list[str]: - m = self._matrix() - if m is None: - return [] - _, names, _ = m - # Deduplicate while preserving order — 10x panels can repeat a symbol. - seen, out = set(), [] - for g in names: - if g not in seen: - seen.add(g) - out.append(g) - return out - - def cell_expression(self, cell_id: str) -> dict: - m = self._matrix() - if m is None: - return {} - barcodes, names, mat = m - try: - idx = barcodes.index(str(cell_id)) - except ValueError: - return {} - col = mat.getcol(idx).toarray().ravel() - nz = np.nonzero(col)[0] - return {names[i]: int(col[i]) for i in nz} - - # ── Colour values ───────────────────────────────────────────────────────── - - def color_values(self, mode: str, field: Optional[str] = None, - genes: Optional[list[str]] = None, - categorical: Optional[bool] = None) -> dict: - if mode == "gene_set": - return self._color_values_gene_set(genes or []) - return self._color_values_meta(field or "", categorical) - - def _metadata_frame(self): - return self._cells_full() - - def _color_values_gene_set(self, genes: list[str]) -> dict: - empty = {"type": "continuous", "values": {}, "min": 0.0, "max": 0.0} - m = self._matrix() - if m is None or not genes: - return empty - barcodes, names, mat = m - wanted = set(genes) - rows = [i for i, g in enumerate(names) if g in wanted] - if not rows: - return empty - summed = np.asarray(mat[rows, :].sum(axis=0)).ravel() - vmax = float(summed.max()) if summed.size and summed.max() > 0 else 1.0 - return { - "type": "continuous", - "values": {barcodes[i]: float(summed[i]) for i in range(len(barcodes))}, - "min": 0.0, - "max": vmax, - } - diff --git a/backend/app/readers/visium_reader.py b/backend/app/readers/visium_reader.py new file mode 100644 index 0000000..f998538 --- /dev/null +++ b/backend/app/readers/visium_reader.py @@ -0,0 +1,175 @@ +""" +10x Genomics Visium (classic v1/v2) reader. + +The original Visium slide: a 6.5 × 6.5 mm capture area carrying 4,992 barcoded +spots, each **55 µm across on a 100 µm hexagonal pitch**. One to ten cells per +spot, so a spot is a neighbourhood rather than a cell. + +Expected directory layout (Space Ranger `outs/`) +------------------------------------------------ + dataset_dir/ + filtered_feature_bc_matrix.h5 + spatial/ + tissue_positions.csv barcode, in_tissue, array_row, array_col, + pxl_row_in_fullres, pxl_col_in_fullres + scalefactors_json.json spot_diameter_fullres, tissue_hires_scalef, + tissue_lowres_scalef, fiducial_diameter_fullres + tissue_hires_image.png + tissue_lowres_image.png + +Point the dataset folder at the contents of `outs/`, not at its parent — the same +convention every other reader here follows, and what the morphology-image scan +(root plus one subdirectory) expects. + +Three things differ from Visium HD, and all three are traps +----------------------------------------------------------- +1. **There is no `microns_per_pixel`.** Classic Visium scalefactors carry only the + four keys above; 10x deliberately does not record image pixel size, noting that + "prior knowledge of the image pixel sizes is not used". So `pixel_size` is + *derived* from the one physical constant the slide guarantees — a spot is + 55 µm — giving `55 / spot_diameter_fullres` µm per fullres pixel. + + 10x cautions that spot diameters are estimates and recommends a calibrated + microscope value instead. That caveat is inherited here: distances in the + measurement tool, and the placement of `edges.parquet` micron coordinates, are + only as good as that estimate. It is reported as `pixel_size_derived: true` in + `info()` so the uncertainty is visible rather than implied. + +2. **`tissue_positions_list.csv` (Space Ranger < 2.0) has no header row.** Reading + it with pandas' default header inference eats the first spot and mislabels + every column. Handled in `spaceranger.py::_read_positions_table`. + +3. **`tissue_hires_scalef` is genuinely far from 1.** On classic Visium the hires + PNG is ~2,000 px on its long edge against a fullres slide image an order of + magnitude larger, so the factor runs around 0.08. Skip the multiply and every + spot lands roughly 12× off the image. See the `spaceranger.py` docstring. + +Spots are rendered as circles +----------------------------- +`cell_boundaries()` emits a regular polygon inscribing the 55 µm spot. Squares +would be wrong here in a way they are not for HD — an HD bin really is a square, +whereas a Visium spot is round, and drawing a square grid over a hex lattice +misrepresents both the shape and the gaps between spots. +""" +import math +from pathlib import Path +from typing import Optional + +from app.readers.spaceranger import SpaceRangerReader + +# The physical spot diameter, fixed by the slide design. 10x: "Each spot is +# 55 µm in diameter with a 100 µm center to center distance between spots." +SPOT_DIAMETER_UM = 55.0 + +# Vertices per spot polygon. Enough that a spot reads as round at the zoom levels +# where individual spots are distinguishable, and cheap: a full capture area is +# 4,992 spots, so ~80K vertices — two orders of magnitude below a Visium HD bin +# grid, which is why this can afford a rounder shape than HD's four corners. +SPOT_VERTICES = 16 + +_FALLBACK_MICRONS_PER_PIXEL = 1.0 + + +class VisiumReader(SpaceRangerReader): + + _LOG = "visium" + + # ── Space Ranger hooks ──────────────────────────────────────────────────── + + def _spatial_dir(self) -> Optional[Path]: + d = self.path / "spatial" + return d if d.is_dir() else None + + def _matrix_path(self) -> Optional[Path]: + for name in ("filtered_feature_bc_matrix.h5", "raw_feature_bc_matrix.h5"): + p = self.path / name + if p.exists(): + return p + return None + + def _microns_per_fullres_px(self) -> float: + """Derived from the 55 µm spot spec — classic Visium records no pixel size. + + Falls back to 1.0 rather than raising: an unusable `spot_diameter_fullres` + should degrade the µm labels, not stop the dataset from rendering. + """ + try: + d = float(self._scalefactors()["spot_diameter_fullres"]) + if d > 0: + return SPOT_DIAMETER_UM / d + except (KeyError, TypeError, ValueError): + pass + return _FALLBACK_MICRONS_PER_PIXEL + + def _unit_diameter_fullres(self) -> float: + try: + d = float(self._scalefactors()["spot_diameter_fullres"]) + if d > 0: + return d + except (KeyError, TypeError, ValueError): + pass + return 1.0 + + @staticmethod + def _unit_offsets(radius: float) -> tuple: + """A regular SPOT_VERTICES-gon inscribing the spot circle. + + Counter-clockwise, first vertex not repeated — deck.gl closes the ring. + """ + step = 2.0 * math.pi / SPOT_VERTICES + return tuple( + (radius * math.cos(i * step), radius * math.sin(i * step)) + for i in range(SPOT_VERTICES) + ) + + # ── Identity ────────────────────────────────────────────────────────────── + + @property + def platform(self) -> str: + return "visium" + + def info(self) -> dict: + sf = self._scalefactors() + pos = self._positions() + return { + "platform": self.platform, + "n_spots": int(len(pos)), + "spot_diameter_um": SPOT_DIAMETER_UM, + "spot_diameter_fullres": sf.get("spot_diameter_fullres"), + "spot_diameter_px": self.unit_diameter_px(), + "pixel_size": self.pixel_size, + # Flagged because it is inferred from the spot size rather than + # recorded by the instrument — see the module docstring. + "pixel_size_derived": True, + "tissue_hires_scalef": self.hires_scalef, + "tissue_lowres_scalef": sf.get("tissue_lowres_scalef"), + } + + def capabilities(self) -> dict: + return { + "has_morphology": self._morphology_exists(), + # Spot-level UMI counts only; there are no molecule coordinates. + "has_transcripts": False, + "has_boundaries": self._spatial_dir() is not None, + "unit_label": "spot", + } + + # ── Detection helper ────────────────────────────────────────────────────── + + @staticmethod + def looks_like_visium(path: Path) -> bool: + """True for a classic Visium `outs/` folder. + + Requires both the scalefactors and a positions table: `spatial/` alone is + too weak, since Visium HD also has a top-level `spatial/` folder — though + that one holds images only, which is what keeps the two from colliding. + Registered after Visium HD regardless, so the more specific sentinel wins. + """ + spatial = path / "spatial" + if not (spatial / "scalefactors_json.json").exists(): + return False + return any((spatial / n).exists() for n in ( + "tissue_positions.csv", + "tissue_positions_list.csv", + "tissue_positions.parquet", + )) diff --git a/backend/tests/golden_baseline.json b/backend/tests/golden_baseline.json index d362a25..a40f6a0 100644 --- a/backend/tests/golden_baseline.json +++ b/backend/tests/golden_baseline.json @@ -1220,6 +1220,186 @@ "pixel_size": 1.003128, "platform": "visium_hd" }, + "visium_tiny": { + "bounds_full": { + "digest": "394f7bcf07a3f07e", + "keys": [ + "cell_id", + "vertex_x", + "vertex_y" + ], + "n": 4032, + "n_cells": 252, + "total": 252 + }, + "bounds_half": { + "digest": "bfe707b219830296", + "n": 2016, + "n_cells": 126, + "total": 252 + }, + "capabilities": { + "has_boundaries": true, + "has_morphology": true, + "has_transcripts": false, + "unit_label": "spot" + }, + "cell_detail_0": "db9145f79d454125", + "cell_expression_0": { + "digest": "fbd1e055a2334e0d", + "n": 36 + }, + "cells_all": { + "digest": "bec7aab20a3ae593", + "keys": [ + "array_col", + "array_row", + "cell_id", + "x_centroid", + "y_centroid" + ], + "n": 252 + }, + "cells_schema": { + "columns": { + "array_col": "int64", + "array_row": "int64", + "x_centroid": "float64", + "y_centroid": "float64" + } + }, + "color_forced_cat__array_col": { + "categories_digest": "aa01ea4eeedcd6e7", + "digest": "9224800633773538", + "n": 252, + "n_categories": 40, + "type": "categorical" + }, + "color_forced_cont__array_row": { + "categories_digest": null, + "digest": "ef25fdf2deab9315", + "n": 252, + "n_categories": null, + "type": "continuous" + }, + "color_gene_set": { + "digest": "99e5cd288d788b5b", + "max": 128.0, + "min": 0.0, + "n": 252, + "type": "continuous" + }, + "color_meta__array_row": { + "digest": "87275e1e655af11f", + "n": 252, + "type": "categorical" + }, + "color_meta__x_centroid": { + "digest": "df43e95e72d0a203", + "n": 252, + "type": "continuous" + }, + "color_meta__y_centroid": { + "digest": "8e6e7ad0e7ae2112", + "n": 252, + "type": "continuous" + }, + "edge__edges.parquet__catalogue": { + "digest": "24dd3ff8d4361233", + "n": 30 + }, + "edge__edges.parquet__color_lrm": { + "digest": "191fa6a66f988117" + }, + "edge__edges.parquet__detail0": "ad6b47ab3dad15bd", + "edge__edges.parquet__filter__is_autocrine": { + "digest": "7db54e9548c42b48", + "keys": [ + "edge", + "is_autocrine", + "lrm_count", + "receiving_cell", + "receiving_type", + "score_sum", + "sending_cell", + "sending_type", + "x1", + "x2", + "y1", + "y2" + ], + "n": 1390 + }, + "edge__edges.parquet__grouped": { + "digest": "43b2a883106031e3", + "keys": [ + "edge", + "is_autocrine", + "lrm_count", + "receiving_cell", + "receiving_type", + "score_sum", + "sending_cell", + "sending_type", + "x1", + "x2", + "y1", + "y2" + ], + "n": 1642 + }, + "edge__edges.parquet__schema": { + "columns": { + "edge": "string", + "is_autocrine": "bool", + "ligand": "string", + "lrm": "string", + "lrm_id": "int32", + "receiving_cell": "string", + "receiving_type": "string", + "receptor": "string", + "score": "double", + "score_norm": "double", + "sending_cell": "string", + "sending_type": "string", + "x1": "double", + "x2": "double", + "y1": "double", + "y2": "double" + } + }, + "edge__edges.parquet__scores": { + "digest": "1a286a5384f4cfb4", + "keys": [ + "edge", + "visible_lrm_count", + "visible_score_sum" + ], + "n": 1642 + }, + "filter_bounds__array_row": { + "n_cells": 18, + "total": 18 + }, + "filter_ids__array_row": 18, + "gene_list": { + "digest": "66064248776a3e2f", + "n": 36 + }, + "info_keys": [ + "n_spots", + "pixel_size", + "pixel_size_derived", + "platform", + "spot_diameter_fullres", + "spot_diameter_px", + "spot_diameter_um", + "tissue_hires_scalef", + "tissue_lowres_scalef" + ], + "pixel_size": 3.125, + "platform": "visium" + }, "xenium_human_breast_2fov": { "bounds_full": { "digest": "2e6916032112a6e5", diff --git a/backend/tests/golden_snapshot.py b/backend/tests/golden_snapshot.py index f5f4ddb..ab3a347 100644 --- a/backend/tests/golden_snapshot.py +++ b/backend/tests/golden_snapshot.py @@ -50,6 +50,7 @@ "seqfish_synthetic", # seqFISH, committed — generated by make_seqfish.py "seqfish_instrument2", # seqFISH, local only (licence-restricted) "visium_hd_tiny", # Visium HD, committed — 10x tiny dataset, CC BY 4.0 + "visium_tiny", # Visium classic, committed — synthetic, make_visium.py "merscope-vpt-smallset", # MERSCOPE, local only (41 MB; Vizgen VPT test data) "cosmx-mousebrain", # CosMx, local only (140 MB; OSTA/OSF mouse brain) ] diff --git a/frontend/package.json b/frontend/package.json index 32602f2..28e6b20 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -1,6 +1,6 @@ { "name": "tissueplex", - "version": "0.8.0", + "version": "0.9.0", "private": true, "scripts": { "dev": "vite", diff --git a/frontend/src/components/LayerPanel.jsx b/frontend/src/components/LayerPanel.jsx index 2be13f0..89eeff8 100644 --- a/frontend/src/components/LayerPanel.jsx +++ b/frontend/src/components/LayerPanel.jsx @@ -225,6 +225,7 @@ export default function LayerPanel() { // ── Color By section ────────────────────────────────────────────────────────── function ColorBySection({ unitLabel = "cell" }) { const { + platformCapabilities, apiBase, dataset, cellColorEnabled, setCellColorEnabled, colorBy, setColorBy, @@ -238,6 +239,8 @@ function ColorBySection({ unitLabel = "cell" }) { } = useStore(); const [cellSchema, setCellSchema] = useState(null); + const hasTranscripts = platformCapabilities?.has_transcripts ?? true; + const unitTitle = unitLabel.charAt(0).toUpperCase() + unitLabel.slice(1); // Fetch full gene list and cell schema once per dataset useEffect(() => { @@ -294,14 +297,19 @@ function ColorBySection({ unitLabel = "cell" }) { > - + - {/* Gene set info */} + {/* Gene set info. The "use Transcript Species" pointer is only shown + where that section exists — platforms without molecule coordinates + (Visium, Visium HD) hide it, so the hint would name a control the + user cannot find. Those platforms always sum the whole panel. */} {mode === "gene_set" && (
{selectedCount} of {allGenes.length} genes selected - (use Transcript Species to adjust) + + {hasTranscripts ? " (use Transcript Species to adjust)" : " (whole panel)"} +
)} diff --git a/r/README.md b/r/README.md index 479b6e8..c7e01ac 100644 --- a/r/README.md +++ b/r/README.md @@ -9,16 +9,17 @@ but the first two steps differ enough per platform to be worth reading separatel | `niches_xenium.R` | Xenium | The simple case. Coordinates are already µm; nothing to convert. **Read this first.** | | `niches_seqfish.R` | seqFISH (Spatial Genomics GenePS) | Counts are a dense cells × genes CSV needing transposition; coordinate units vary by GenePS version, so they are detected and reported. | | `niches_visium_hd.R` | Visium HD | Coordinates are **pixels** and must be multiplied by `microns_per_pixel` before NICHESv2 sees them. | +| `niches_visium.R` | Visium (classic) | Same pixel problem, but there is no `microns_per_pixel` to multiply by — it is derived from the 55 µm spot spec, then sanity-checked against the known 100 µm lattice pitch. | | `niches_common.R` | — | Shared helpers only: the 10x HDF5 reader, barcode alignment, LR-coverage check, output validation. Not runnable. | The older `*_PPLR.R` scripts are a personal analysis pipeline with hardcoded paths, kept -for reference. The four above are the ones to copy. +for reference. The five above are the ones to copy. ## Setup ```r install.packages(c("arrow", "Matrix", "jsonlite", "remotes")) -BiocManager::install("rhdf5") # Xenium / Visium HD +BiocManager::install("rhdf5") # Xenium / Visium remotes::install_github("RaredonLab/NICHESv2", ref = "dev") # note: dev, not main ``` @@ -31,6 +32,7 @@ NICHESv2's `Suggests`, so both of those lines matter. Rscript r/niches_xenium.R sample_data/xenium_human_breast_2fov --species human --rad 30 Rscript r/niches_seqfish.R sample_data/seqfish_synthetic --species mouse --rad 12 Rscript r/niches_visium_hd.R sample_data/visium_hd_tiny --bin square_016um --rad 40 +Rscript r/niches_visium.R sample_data/visium_tiny --species mouse --rad 150 ``` Each writes `edges.parquet` into the dataset folder, where TissuePlex picks it up with no diff --git a/r/niches_visium.R b/r/niches_visium.R new file mode 100644 index 0000000..df9163a --- /dev/null +++ b/r/niches_visium.R @@ -0,0 +1,187 @@ +#!/usr/bin/env Rscript +## niches_visium.R ------------------------------------------------------------- +## +## Run NICHESv2 on a classic 10x Visium dataset and write edges.parquet for +## TissuePlex. +## +## Rscript r/niches_visium.R [dataset_dir] [--rad 150] [--out edges.parquet] +## Rscript r/niches_visium.R sample_data/visium_tiny +## +## Read niches_visium_hd.R first — the coordinate problem is the same and is +## explained there at length. The short version: tissue_positions stores +## FULL-RESOLUTION PIXELS, export_to_TissuePlex() copies meta.data$x/$y straight +## into x1..y2, and the backend divides those by pixel_size assuming microns. +## Feed it raw pixels and every edge lands offset from its spot. +## +## The one thing classic Visium does differently, and it matters +## -------------------------------------------------------------- +## There is no `microns_per_pixel` in scalefactors_json.json. 10x does not record +## the image pixel size for classic Visium. So the conversion has to come from the +## one physical constant the slide guarantees: +## +## a Visium spot is 55 um across +## => microns_per_pixel = 55 / spot_diameter_fullres +## +## 10x notes that spot diameters are estimates and recommends a calibrated +## microscope value where you have one. Pass --mpp to override. +## +## Spots are not cells. A 55 um spot holds roughly 1-10 cells, so an edge between +## two spots is a neighbourhood statistic, not a cell-cell claim. Say so in +## figure legends. The 100 um centre-to-centre pitch also sets the sensible `rad` +## floor: below ~110 um no spot has any neighbour at all. +## +## Inputs (Space Ranger `outs/`) +## filtered_feature_bc_matrix.h5 +## spatial/tissue_positions.csv (or tissue_positions_list.csv pre-SR-2.0) +## spatial/scalefactors_json.json +## +## Requires: NICHESv2 (dev branch), arrow, rhdf5, Matrix, jsonlite. + +suppressPackageStartupMessages({ + library(Matrix); library(arrow); library(jsonlite); library(NICHESv2) +}) + +.this <- tryCatch({ + a <- commandArgs(trailingOnly = FALSE) + normalizePath(sub("^--file=", "", a[grep("^--file=", a)][1])) +}, error = function(e) NA_character_) +source(file.path(if (is.na(.this)) "r" else dirname(.this), "niches_common.R")) + +# The slide spec: "Each spot is 55 um in diameter with a 100 um center to center +# distance between spots." +SPOT_DIAMETER_UM <- 55 +SPOT_PITCH_UM <- 100 + +# ── Parameters ──────────────────────────────────────────────────────────────── +args <- commandArgs(trailingOnly = TRUE) +opt <- function(flag, default) { + i <- match(flag, args); if (is.na(i) || i == length(args)) default else args[i + 1L] +} +positional <- args[!grepl("^--", args) & + !(seq_along(args) %in% (which(grepl("^--", args)) + 1L))] + +DATASET <- if (length(positional)) positional[1] else "sample_data/visium_tiny" +OUT <- opt("--out", file.path(DATASET, "edges.parquet")) +SPECIES <- opt("--species", "mouse") +# rad in MICRONS. The lattice pitch is 100 um, so 150 um reaches the six +# immediate neighbours and nothing beyond; 250 um reaches the second ring. +RAD <- as.numeric(opt("--rad", "150")) +CORES <- as.integer(opt("--cores", "4")) +MINCOUNT <- as.integer(opt("--min-counts", "1")) +# Override the derived pixel size if you have a calibrated value (um per +# full-resolution pixel). +MPP_ARG <- suppressWarnings(as.numeric(opt("--mpp", NA))) + +cat("── NICHESv2 · Visium (classic) ───────────────────────────────\n") +cat(" dataset :", DATASET, "\n output :", OUT, "\n") +cat(" species :", SPECIES, " rad:", RAD, "um cores:", CORES, "\n\n") + +if (RAD < SPOT_PITCH_UM * 1.1) + warning("rad ", RAD, " um is below the ", SPOT_PITCH_UM, + " um spot pitch — most spots will have no neighbours.") + +# ── 1. Counts ───────────────────────────────────────────────────────────────── +cat("reading counts…\n") +h5 <- file.path(DATASET, "filtered_feature_bc_matrix.h5") +if (!file.exists(h5)) stop("no filtered_feature_bc_matrix.h5 in ", DATASET, + " — point at the contents of Space Ranger's outs/") +count.mtx <- read_10x_h5(h5) +cat(sprintf(" %d genes x %d spots\n", nrow(count.mtx), ncol(count.mtx))) + +# ── 2. Positions ────────────────────────────────────────────────────────────── +cat("reading spot positions…\n") +pos.cols <- c("barcode", "in_tissue", "array_row", "array_col", + "pxl_row_in_fullres", "pxl_col_in_fullres") +pos.modern <- file.path(DATASET, "spatial", "tissue_positions.csv") +pos.legacy <- file.path(DATASET, "spatial", "tissue_positions_list.csv") +if (file.exists(pos.modern)) { + pos <- read.csv(pos.modern) +} else if (file.exists(pos.legacy)) { + # Space Ranger < 2.0 wrote this file with NO header row. Reading it with + # header = TRUE silently eats the first spot and mislabels every column. + pos <- read.csv(pos.legacy, header = FALSE, col.names = pos.cols) +} else { + stop("no tissue_positions.csv or tissue_positions_list.csv in ", + file.path(DATASET, "spatial")) +} +pos <- pos[pos$in_tissue == 1, , drop = FALSE] +cat(sprintf(" %d in-tissue spots\n", nrow(pos))) + +# ── 3. The conversion: fullres pixels -> microns ───────────────────────────── +sf.path <- file.path(DATASET, "spatial", "scalefactors_json.json") +sf <- jsonlite::fromJSON(sf.path) +if (is.finite(MPP_ARG) && MPP_ARG > 0) { + mpp <- MPP_ARG + cat(sprintf(" microns_per_pixel: %.6f (supplied via --mpp)\n", mpp)) +} else { + spot.px <- as.numeric(sf$spot_diameter_fullres) + if (!is.finite(spot.px) || spot.px <= 0) + stop("spot_diameter_fullres missing or invalid in ", sf.path, + " — pass --mpp instead") + mpp <- SPOT_DIAMETER_UM / spot.px + cat(sprintf(" microns_per_pixel: %.6f (DERIVED from %.1f um / %.1f px)\n", + mpp, SPOT_DIAMETER_UM, spot.px)) + cat(" note: 10x calls spot diameters estimates; pass --mpp if you have a\n") + cat(" calibrated microscope value.\n") +} + +meta.data <- data.frame( + x = as.numeric(pos$pxl_col_in_fullres) * mpp, + y = as.numeric(pos$pxl_row_in_fullres) * mpp, + row.names = as.character(pos$barcode), + stringsAsFactors = FALSE +) +report_extent(meta.data, "um") + +# Sanity check the derivation against the known lattice pitch: nearest-neighbour +# distance on a Visium slide must come out near 100 um. If it does not, either +# spot_diameter_fullres is wrong or the positions are not in fullres pixels, and +# every downstream distance is off by the same factor. +if (nrow(meta.data) > 1) { + s <- meta.data[sample(nrow(meta.data), min(200, nrow(meta.data))), ] + d <- as.matrix(dist(s[, c("x", "y")])) + diag(d) <- Inf + nn <- median(apply(d, 1, min)) + cat(sprintf(" median nearest-neighbour distance: %.1f um (expect ~%d)\n", + nn, SPOT_PITCH_UM)) + if (nn < SPOT_PITCH_UM * 0.5 || nn > SPOT_PITCH_UM * 2) + warning("nearest-neighbour spacing is far from the ", SPOT_PITCH_UM, + " um Visium pitch — check spot_diameter_fullres or pass --mpp.") +} + +aligned <- align_counts_meta(count.mtx, meta.data, "spots") +count.mtx <- aligned$count.mtx +meta.data <- aligned$meta.data + +if (MINCOUNT > 0) { + keep <- Matrix::colSums(count.mtx) >= MINCOUNT + cat(sprintf(" dropping %d spots with < %d counts (%d remain)\n", + sum(!keep), MINCOUNT, sum(keep))) + count.mtx <- count.mtx[, keep, drop = FALSE] + meta.data <- meta.data[keep, , drop = FALSE] +} + +# ── 4. Build and export ─────────────────────────────────────────────────────── +check_lr_coverage(count.mtx, SPECIES) + +cat("\nrunning NICHESv2 (spatial mode)…\n") +obj <- create_NICHESObject( + count.mtx = count.mtx, + meta.data = meta.data, + LRM.db = "connectomedb2025", + species = SPECIES, + mode = "spatial", + rad = RAD, # microns, matching meta.data$x/$y + method = "product", + normalize.method = "prop", + n.cores = CORES, + verbose = TRUE +) + +cat("\nexporting…\n") +export_to_TissuePlex(obj, output.path = OUT, + x.col = "x", y.col = "y", + celltype.col = NULL, n.threads = CORES) + +validate_edges_parquet(OUT) +cat("\ndone — reload TissuePlex to see the edge layer.\n") diff --git a/sample_data/make_visium.py b/sample_data/make_visium.py new file mode 100644 index 0000000..5c64067 --- /dev/null +++ b/sample_data/make_visium.py @@ -0,0 +1,252 @@ +#!/usr/bin/env python3 +""" +Generate a tiny synthetic classic Visium (10x, v1/v2) dataset. + +Why this exists +--------------- +Real Space Ranger output for a classic Visium run is a few hundred MB, and the +public 10x datasets are not redistributable from this repo. Without a committable +fixture the Visium reader has nothing to test against in a fresh checkout. + +What it deliberately gets *awkward*, because that is what a reader gets wrong: + + * **``tissue_hires_scalef`` is not 1.0.** This is the whole point. Space Ranger + reports spot positions in full-resolution pixels but only ships the *hires* + PNG, so coordinates must be multiplied by that factor to land on the image. + ``sample_data/visium_hd_tiny`` has the factor set to exactly 1.0, which is + why a missing multiply went unnoticed there for a whole release. This fixture + uses 0.08 — the realistic order of magnitude — so the same bug fails loudly. + * **No ``microns_per_pixel`` key.** Classic Visium scalefactors carry only the + four keys 10x actually writes, forcing ``pixel_size`` down the derived path + (``55 µm / spot_diameter_fullres``) rather than the recorded one. + * **A hexagonal lattice with the odd-row offset**, not a square grid, and with + off-tissue spots present and flagged ``in_tissue = 0`` so the filter is + exercised. + +Usage +----- + python make_visium.py --out visium_tiny + python make_visium.py --out visium_tiny --rows 24 --cols 32 --seed 7 + python make_visium.py --out visium_legacy --legacy-positions # pre-SR-2.0 + +Writes /{filtered_feature_bc_matrix.h5, spatial/{tissue_positions.csv, +scalefactors_json.json, tissue_hires_image.png, tissue_lowres_image.png}}. +""" +from __future__ import annotations + +import argparse +import json +import string +from pathlib import Path + +import numpy as np + +# Slide geometry, fixed by 10x: 55 µm spots on a 100 µm centre-to-centre pitch. +SPOT_DIAMETER_UM = 55.0 +SPOT_PITCH_UM = 100.0 + +# Deliberately far from 1.0 — see the module docstring. +HIRES_SCALEF = 0.08 +LOWRES_SCALEF = 0.024 + +# µm per full-resolution pixel. A realistic 20x brightfield scan. +MICRONS_PER_FULLRES_PX = 0.25 + +# Real mouse gene symbols forming complete ligand-receptor pairs in +# connectomedb2025, so NICHESv2 can actually score this fixture — the same +# reasoning as make_seqfish.py, where an invented panel made the edge pipeline +# impossible to demonstrate. +LR_PANEL = [ + "Tgfb1", "Tgfbr1", "Tgfbr2", "Wnt5a", "Fzd1", "Ror2", "Cxcl12", "Cxcr4", + "Vegfa", "Kdr", "Flt1", "Pdgfb", "Pdgfrb", "Hgf", "Met", "Il6", "Il6ra", + "Efnb1", "Ephb2", "Spp1", "Cd44", "Ccl2", "Ccr2", "Bmp4", "Bmpr2", + "Fgf2", "Fgfr1", "Egf", "Egfr", "Jag1", "Dll4", "Notch1", "Apoe", "Lrp1", + "Thbs1", "Cd47", +] + + +def barcode(i: int) -> str: + """A plausible 16 nt Visium barcode with the standard ``-1`` suffix.""" + letters = "ACGT" + out = [] + n = i + for _ in range(16): + out.append(letters[n % 4]) + n //= 4 + return "".join(out) + "-1" + + +def build_positions(rows: int, cols: int, rng) -> list[dict]: + """Hex lattice in fullres pixels, with an off-tissue margin. + + Visium's lattice offsets odd rows by half a pitch, which is why + ``array_col`` steps by 2 within a row in real output. Reproduced so anything + that reasons about the grid sees the real shape. + """ + pitch_px = SPOT_PITCH_UM / MICRONS_PER_FULLRES_PX + # Row spacing on a hex lattice is pitch * sqrt(3)/2. + row_px = pitch_px * np.sqrt(3.0) / 2.0 + margin = pitch_px * 1.5 + + # An ellipse of "tissue" inside the capture area; everything outside it is + # captured but flagged in_tissue = 0, as on a real slide. + cx, cy = (cols - 1) / 2.0, (rows - 1) / 2.0 + rx, ry = cols * 0.36, rows * 0.36 + + out = [] + idx = 0 + for r in range(rows): + for c in range(cols): + x = margin + (c * pitch_px) + (pitch_px / 2.0 if r % 2 else 0.0) + y = margin + (r * row_px) + inside = ((c - cx) / rx) ** 2 + ((r - cy) / ry) ** 2 <= 1.0 + out.append({ + "barcode": barcode(idx), + "in_tissue": 1 if inside else 0, + "array_row": r, + # Real Visium numbers columns in steps of 2, offset on odd rows. + "array_col": c * 2 + (1 if r % 2 else 0), + "pxl_row_in_fullres": float(y), + "pxl_col_in_fullres": float(x), + }) + idx += 1 + return out + + +def write_matrix(path: Path, barcodes: list[str], genes: list[str], counts) -> None: + """Write a 10x-format filtered_feature_bc_matrix.h5 (CSC, genes × spots).""" + import h5py + import scipy.sparse as sp + + mat = sp.csc_matrix(counts) + with h5py.File(path, "w") as f: + g = f.create_group("matrix") + g.create_dataset("barcodes", data=np.array(barcodes, dtype="S")) + g.create_dataset("data", data=mat.data.astype(np.int32)) + g.create_dataset("indices", data=mat.indices.astype(np.int64)) + g.create_dataset("indptr", data=mat.indptr.astype(np.int64)) + g.create_dataset("shape", data=np.array(mat.shape, dtype=np.int32)) + feat = g.create_group("features") + feat.create_dataset("name", data=np.array(genes, dtype="S")) + feat.create_dataset("id", data=np.array( + [f"ENSMUSG{i:011d}" for i in range(len(genes))], dtype="S")) + feat.create_dataset("feature_type", data=np.array( + ["Gene Expression"] * len(genes), dtype="S")) + feat.create_dataset("genome", data=np.array(["mm10"] * len(genes), dtype="S")) + + +def write_image(path: Path, w: int, h: int, spots, radius: float, rng) -> None: + """A grey H&E-ish PNG with a faint blob under each in-tissue spot. + + Not decorative: the reader's whole coordinate contract is "these pixels are + the hires image", so a fixture whose image dimensions do not match the scaled + coordinates would let a broken scaling pass. + """ + from PIL import Image + + img = np.full((h, w, 3), 236, dtype=np.uint8) + img[..., 2] = 240 + yy, xx = np.mgrid[0:h, 0:w] + for s in spots: + cx, cy = s["hx"], s["hy"] + # Bounding box around the spot; skip the full-frame distance computation. + x0, x1 = max(0, int(cx - radius)), min(w, int(cx + radius) + 1) + y0, y1 = max(0, int(cy - radius)), min(h, int(cy + radius) + 1) + if x1 <= x0 or y1 <= y0: + continue + d = np.hypot(xx[y0:y1, x0:x1] - cx, yy[y0:y1, x0:x1] - cy) + m = d <= radius + img[y0:y1, x0:x1][m] = ( + img[y0:y1, x0:x1][m] * 0.55 + np.array([120, 80, 150]) * 0.45 + ).astype(np.uint8) + Image.fromarray(img).save(path) + + +def main() -> int: + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--out", default="visium_tiny") + ap.add_argument("--rows", type=int, default=22) + ap.add_argument("--cols", type=int, default=28) + ap.add_argument("--seed", type=int, default=42) + ap.add_argument("--legacy-positions", action="store_true", + help="write headerless tissue_positions_list.csv (Space Ranger < 2.0)") + args = ap.parse_args() + + rng = np.random.default_rng(args.seed) + out = Path(args.out) + (out / "spatial").mkdir(parents=True, exist_ok=True) + + positions = build_positions(args.rows, args.cols, rng) + in_tissue = [p for p in positions if p["in_tissue"] == 1] + + spot_diameter_fullres = SPOT_DIAMETER_UM / MICRONS_PER_FULLRES_PX + + # Image is the fullres frame scaled by HIRES_SCALEF. + max_x = max(p["pxl_col_in_fullres"] for p in positions) + max_y = max(p["pxl_row_in_fullres"] for p in positions) + pad = spot_diameter_fullres * 1.5 + hires_w = int(round((max_x + pad) * HIRES_SCALEF)) + hires_h = int(round((max_y + pad) * HIRES_SCALEF)) + + # ── positions ───────────────────────────────────────────────────────────── + cols = ["barcode", "in_tissue", "array_row", "array_col", + "pxl_row_in_fullres", "pxl_col_in_fullres"] + lines = [] + if not args.legacy_positions: + lines.append(",".join(cols)) + for p in positions: + lines.append(",".join(str(p[c]) for c in cols)) + name = "tissue_positions_list.csv" if args.legacy_positions else "tissue_positions.csv" + (out / "spatial" / name).write_text("\n".join(lines) + "\n") + + # ── scalefactors: exactly the four keys 10x writes, and no more ─────────── + (out / "spatial" / "scalefactors_json.json").write_text(json.dumps({ + "spot_diameter_fullres": spot_diameter_fullres, + "fiducial_diameter_fullres": spot_diameter_fullres * 2.5, + "tissue_hires_scalef": HIRES_SCALEF, + "tissue_lowres_scalef": LOWRES_SCALEF, + }, indent=4) + "\n") + + # ── images ──────────────────────────────────────────────────────────────── + hires_spots = [{"hx": p["pxl_col_in_fullres"] * HIRES_SCALEF, + "hy": p["pxl_row_in_fullres"] * HIRES_SCALEF} for p in in_tissue] + write_image(out / "spatial" / "tissue_hires_image.png", hires_w, hires_h, + hires_spots, spot_diameter_fullres * HIRES_SCALEF / 2.0, rng) + lowres_spots = [{"hx": p["pxl_col_in_fullres"] * LOWRES_SCALEF, + "hy": p["pxl_row_in_fullres"] * LOWRES_SCALEF} for p in in_tissue] + write_image(out / "spatial" / "tissue_lowres_image.png", + int(round((max_x + pad) * LOWRES_SCALEF)), + int(round((max_y + pad) * LOWRES_SCALEF)), + lowres_spots, spot_diameter_fullres * LOWRES_SCALEF / 2.0, rng) + + # ── counts ──────────────────────────────────────────────────────────────── + # Three spatial "domains" across the tissue so colour-by and NICHESv2 have + # real structure to find rather than uniform noise. + genes = LR_PANEL + xs = np.array([p["pxl_col_in_fullres"] for p in in_tissue]) + domain = np.digitize(xs, np.quantile(xs, [1 / 3, 2 / 3])) + counts = np.zeros((len(genes), len(in_tissue)), dtype=np.int32) + for j, d in enumerate(domain): + base = rng.poisson(3.0, size=len(genes)) + # Each domain over-expresses one third of the panel. + lo, hi = d * len(genes) // 3, (d + 1) * len(genes) // 3 + base[lo:hi] += rng.poisson(18.0, size=hi - lo) + counts[:, j] = base + write_matrix(out / "filtered_feature_bc_matrix.h5", + [p["barcode"] for p in in_tissue], genes, counts) + + (out / "metrics_summary.csv").write_text( + "Number of Spots Under Tissue,Mean Reads per Spot\n" + f"{len(in_tissue)},{int(counts.sum() / max(1, len(in_tissue)))}\n") + + print(f"wrote {out}/") + print(f" {len(positions)} spots ({len(in_tissue)} in tissue), {len(genes)} genes") + print(f" hires image {hires_w}x{hires_h} px (tissue_hires_scalef={HIRES_SCALEF})") + print(f" spot_diameter_fullres={spot_diameter_fullres:.2f} px " + f"-> derived pixel_size={MICRONS_PER_FULLRES_PX / HIRES_SCALEF:.4f} um/hires-px") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/sample_data/visium_tiny/PROVENANCE.md b/sample_data/visium_tiny/PROVENANCE.md new file mode 100644 index 0000000..f23c2ab --- /dev/null +++ b/sample_data/visium_tiny/PROVENANCE.md @@ -0,0 +1,48 @@ +# visium_tiny — synthetic classic Visium fixture + +Generated, not real data. Reproduce with: + +```bash +python sample_data/make_visium.py --out sample_data/visium_tiny +Rscript r/niches_visium.R sample_data/visium_tiny --rad 150 +``` + +Synthetic because real Space Ranger output is a few hundred MB and the public 10x +Visium datasets are not redistributable from this repo. 788 KB here, including the +NICHESv2 edge layer. + +## What it is for + +Unlike `visium_hd_tiny`, which is real 10x data, this exists to make one specific +bug impossible to reintroduce. + +`tissue_hires_scalef` is **0.08**, not 1.0. + +Space Ranger reports spot positions in full-resolution pixels but only ships the +*hires* PNG, so coordinates must be multiplied by that factor to land on the image. +`visium_hd_tiny` has the factor set to exactly 1.0, so the multiply being absent +was invisible there — and it was in fact absent for a whole release, while the +reader docstring claimed otherwise. The check that catches it: + +| | spots on image | +|---|---| +| with the multiply | 252 / 252 | +| without it | 0 / 252 | + +It also forces the *derived* pixel-size path, because a real classic Visium +`scalefactors_json.json` carries no `microns_per_pixel` — only the four keys 10x +actually writes. The generator uses 0.25 µm per fullres pixel and a 220 px spot +diameter; `55 / 220 = 0.25` recovers it exactly, and the NICHESv2 script's +independent check on the lattice reports a median nearest-neighbour distance of +100.0 µm, which is the Visium pitch. + +## What it is not + +The lattice geometry, the units and the file formats are faithful. **The biology is +not.** Counts are Poisson noise with three arbitrary x-axis domains over a 36-gene +panel chosen to form complete connectomedb2025 ligand-receptor pairs, so NICHESv2 +has something to score. Nothing here should be read as a result. + +No real Space Ranger `outs/` tree has been through this reader yet. 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+CAGCGAAAAAAAAAAA-1,0,21,43,7874.613391789285,9200.0 +GAGCGAAAAAAAAAAA-1,0,21,45,7874.613391789285,9600.0 +TAGCGAAAAAAAAAAA-1,0,21,47,7874.613391789285,10000.0 +ACGCGAAAAAAAAAAA-1,0,21,49,7874.613391789285,10400.0 +CCGCGAAAAAAAAAAA-1,0,21,51,7874.613391789285,10800.0 +GCGCGAAAAAAAAAAA-1,0,21,53,7874.613391789285,11200.0 +TCGCGAAAAAAAAAAA-1,0,21,55,7874.613391789285,11600.0 From 86569ecd047e08b63c0e2cea8e3080a1d429ba69 Mon Sep 17 00:00:00 2001 From: Micha Sam Brickman Raredon <42100128+msraredon@users.noreply.github.com> Date: Thu, 6 Aug 2026 21:21:24 -0400 Subject: [PATCH 02/13] fix: correct the Visium pixel-size derivation against real 10x data MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Testing the reader against real Space Ranger output (V1_Mouse_Kidney and V1_Adult_Mouse_Brain) found the previous commit's `pixel_size` derivation wrong by 18%, and turned up an unrelated autocrine bug on the way. pixel_size now comes from the lattice pitch, not the spot diameter Deriving it as `55 um / spot_diameter_fullres` looked obvious and was wrong. Measured independently on both datasets: the in-row lattice pitch is exactly 138.00 fullres px while spot_diameter_fullres is 89.45 — a ratio of 0.648. That field is Space Ranger's DETECTED spot footprint (~64.8 um), not the 55 um nominal capture spot; 10x's own docs describe classic Visium spot diameters as "approximately 60-70 um" and warn they are estimates. It now derives from the 100 um centre-to-centre pitch, measured off the positions table. That reconstructs 10x's specified 6.5 x 6.5 mm capture area where the old route gave 5.4 x 5.7 mm, and it is better conditioned — a median over thousands of positions in a rigid array template rather than one estimate of a fuzzy edge. `info()` reports `pixel_size_source` so which route was taken is visible. This matters beyond distance labels: EdgeReader divides edges.parquet micron coordinates by pixel_size, so the whole connectivity layer was placed 18% off. The fixture could not have caught it, and now can make_visium.py set spot_diameter_fullres to exactly 55/mpp, making the fixture a tautology — it confirmed whatever derivation the reader used. It now emits the real 0.648 ratio, so the bad derivation yields an 85 um nearest-neighbour spacing against the 100 um truth, the same failure signature the real datasets show. Verified against real data 1438/1438 spots land on the hires image (0/1438 without the scale multiply). Registration: 98.1% of in_tissue spots sit on stained tissue and 99.5% of out-of-tissue spots on bare slide, measured as mean inverse intensity under each spot polygon. NICHESv2 end to end: 9,666 edges, 2,479 LRMs, 3.2M rows, top mechanisms Apoe|Lrp2 and Cst3|Lrp2 — megalin-mediated proximal tubule uptake, the expected kidney biology. Autocrine signalling Autocrine edges were excluded from the lrm_set colour computation, so the rings stayed default orange whatever the colour control said — the one edge type that ignored "colour by LRM set". They also skipped the visible_lrm_count test, so hiding every mechanism removed the lines but left a ring on every cell. Both fixed. Sharing the colour scale is safe: autocrine score medians run 1.00-1.33x the directed medians across the bundled datasets. Verified on Visium, Xenium, seqFISH and Visium HD. User manual (docs/index.html, published to GitHub Pages) Listed three platforms; now lists six with their sentinels and units. Adds sections for metadata filtering and the treat-as-categorical toggle, rewrites the autocrine section, de-Xenium-ifies the intro, and is now linked from the README, which never pointed at it. Also: r/niches_visium.R uses the same corrected derivation with an independent lattice-pitch check; docs/public_datasets.md records the verification downloads; README platform table corrected (MERSCOPE and CosMx have had boundaries since v0.7.1 but were still marked absent). v0.8.2. Guard: 217 probes across 8 datasets. Co-Authored-By: Claude Opus 5 --- CLAUDE.md | 72 ++++++-- README.md | 5 +- backend/app/main.py | 2 +- backend/app/readers/visium_reader.py | 126 ++++++++++++-- backend/tests/golden_baseline.json | 10 +- docs/index.html | 155 ++++++++++++++++-- docs/public_datasets.md | 47 +++++- frontend/package.json | 2 +- frontend/src/components/Viewer.jsx | 9 +- frontend/src/hooks/useEdgeColors.js | 13 +- r/niches_visium.R | 64 ++++++-- sample_data/.gitignore | 4 + sample_data/make_visium.py | 26 ++- sample_data/visium_tiny/PROVENANCE.md | 36 +++- .../spatial/scalefactors_json.json | 4 +- .../spatial/tissue_hires_image.png | Bin 5904 -> 6568 bytes .../spatial/tissue_lowres_image.png | Bin 1540 -> 1608 bytes 17 files changed, 481 insertions(+), 94 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index 1b5589a..70bc2d3 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -76,9 +76,10 @@ and boundary layers rather than returning empty arrays for them. UMI counts only, no molecule coordinates); `unit_label: "bin"`. See its own section. - Visium classic (v1/v2): fully implemented — spots, spot outlines as circles, expression, and both color-value modes. `has_transcripts: False`; - `unit_label: "spot"`. Shares `spaceranger.py` with Visium HD. **Verified only - against the synthetic fixture so far** — no real Space Ranger `outs/` tree has - been through it. See its own section. + `unit_label: "spot"`. Shares `spaceranger.py` with Visium HD. Verified against real + Space Ranger output (`V1_Mouse_Kidney`, `V1_Adult_Mouse_Brain`), including a + registration check: 98.1% of `in_tissue` spots land on stained tissue and 99.5% of + out-of-tissue spots land on bare slide. See its own section. - MERSCOPE: cells, transcripts, genes, color-values (metadata + gene-set) implemented; `cell_boundaries()` returns empty and `has_boundaries: False` (HDF5 polygon format not yet parsed) @@ -193,8 +194,12 @@ docs/ data_format.md edges.parquet column spec for NICHESv2 R export setup.md Docker deployment guide (lab-facing) cloud-deploy.md DigitalOcean deployment runbook (~$106–116/mo) - public_datasets.md Links to public Xenium datasets used for development - index.html, demo.gif Landing page + README demo animation + public_datasets.md Public datasets used for development, and the classic + Visium pair the reader was verified against + index.html The user manual, published to GitHub Pages at + https://raredonlab.github.io/TissuePlex/ — hand-written + HTML, no build step. Update it when a UI control changes. + demo.gif README demo animation OBS/ Archived, superseded planning docs. Provenance only — NOT a specification. See OBS/README.md. NICHESv2_package_design.md Design doc for the separate NICHESv2 R package (not this repo) @@ -724,14 +729,44 @@ dataset_dir/ ← point at the *contents* of Space Ranger's Three things differ from Visium HD, and all three are traps: -- **There is no `microns_per_pixel`.** Classic Visium scalefactors carry only those four - keys; 10x deliberately does not record image pixel size, noting that "prior knowledge - of the image pixel sizes is not used". `pixel_size` is therefore *derived* from the one - constant the slide guarantees — `55 / spot_diameter_fullres`. 10x cautions that spot - diameters are estimates and recommends a calibrated microscope value, so the derivation - is surfaced as `pixel_size_derived: true` in `info()` rather than passed off as - measured. Everything downstream inherits that uncertainty: the measurement tool, and - the placement of `edges.parquet` micron coordinates. +- **There is no `microns_per_pixel`**, and the obvious replacement is wrong by 18%. + Classic Visium scalefactors carry only those four keys; 10x deliberately does not record + image pixel size, noting that "prior knowledge of the image pixel sizes is not used". + So `pixel_size` has to be derived — from the **100 µm lattice pitch**, measured off the + positions table, *not* from `spot_diameter_fullres`. + + Measured independently on `V1_Mouse_Kidney` and `V1_Adult_Mouse_Brain`: + + | | kidney | brain | + |---|---|---| + | in-row pitch (fullres px) | 138.00 | 138.00 | + | `spot_diameter_fullres` | 89.46 | 89.44 | + | ratio | 0.6482 | 0.6481 | + + `spot_diameter_fullres` is Space Ranger's **detected spot footprint** — ~64.8 µm at that + ratio, not the 55 µm nominal capture diameter. 10x's own docs say as much, describing + classic Visium spot diameters as "approximately 60–70 µm" and warning they are + estimates. Deriving from 55 µm yields 0.615 µm/px where the truth is 0.725, and + reconstructs a 5.4 × 5.7 mm capture area against the specified 6.5 × 6.5 mm; the pitch + derivation reconstructs 6.35 × 6.67 mm. The pitch is also better conditioned — it + averages thousands of positions in a rigid array template, where the diameter is one + estimate of a fuzzy edge. + + **This was shipped wrong and caught only by real data.** The first version of + `make_visium.py` set `spot_diameter_fullres = 55 / microns_per_pixel`, which made the + fixture a tautology: it confirmed whatever derivation the reader used. It now emits the + real 0.648 ratio, so the bad derivation produces an 85 µm nearest-neighbour spacing + against the 100 µm truth — the same failure the real datasets show. + + `info()` reports `pixel_size_source`: `"lattice_pitch"` normally, `"spot_diameter"` when + the positions table is too sparse to measure a pitch (< 20 in-row samples) and the + reader falls back on the 64.8 µm constant. Everything downstream inherits whichever was + used — the measurement tool, and the placement of `edges.parquet` micron coordinates. + +- **Spots are drawn at `spot_diameter_fullres`, i.e. the ~65 µm detected footprint**, not + the 55 µm capture area. That matches Loupe, scanpy and squidpy, so TissuePlex does not + render Visium differently from every other viewer; `info()` reports both + `spot_capture_diameter_um` and `detected_spot_um` so the distinction is visible. - **`tissue_positions_list.csv` (Space Ranger < 2.0) has no header row.** Read with pandas' default header inference it eats the first spot and mislabels every column. `spaceranger.py::_read_positions_table` names the columns explicitly for that filename. @@ -953,6 +988,17 @@ call is made for `lrm_set` mode. The p95 of `visible_score_sum` across the curre viewport is auto-set as `edgeColorClamp.high` so the color range adapts to the data rather than being dominated by outlier edges. +**Autocrine edges are colored on the same scale as directed edges.** They were once +excluded from the `lrm_set` computation, which left the rings stuck on their default +orange whatever the color control said — the one edge type that ignored "color by LRM +set" — and they also skipped the `visible_lrm_count > 0` test, so hiding every +mechanism removed the lines but left a ring on every cell. Both are fixed: the layer +now derives from the same map and applies the same LRM filter. Sharing the scale is +safe because autocrine `score_sum` medians run 1.00–1.33× the directed medians across +the bundled datasets, so they neither dominate the range nor need one of their own. +Metadata mode never had the problem, because `edge_color_values` groups over the whole +parquet and so already covered autocrine rows. + Categorical data uses `QUAL_PALETTE` (20 visually distinct colors) from `colormap.js`. Beyond 20 categories, `geneColor()` provides deterministic hash-based colors. diff --git a/README.md b/README.md index 892f9fd..67d04dd 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,8 @@ An interactive spatial transcriptomics viewer for exploring cell-cell communicat ![TissuePlex demo](docs/demo.gif) +📖 **[User manual](https://raredonlab.github.io/TissuePlex/)** — step-by-step setup and a reference for every control in the interface. No programming experience assumed. + --- ## What it does @@ -15,7 +17,7 @@ Spatial transcriptomics platforms (Xenium, seqFISH, Visium, Visium HD, MERSCOPE, - **Toggle individual LRMs in real time** — select any subset of 100s of ligand-receptor mechanisms and instantly see which cell pairs are communicating through them - **Color edges by communication score or metadata** — visualize LRM set strength, cell type, or any custom column from your analysis as a continuous or categorical color scale - **Click any edge for full detail** — inspect every active LRM for a given cell pair with their individual scores -- **Directed edges with arrowheads** — A→B and B→A are visually distinct; autocrine communication renders as rings +- **Directed edges with arrowheads** — A→B and B→A are visually distinct; autocrine communication renders as a ring per cell, on the same color scale and mechanism filter as the edges - **Multiple edge sets per dataset** — drop several `.parquet` files into an `edges/` folder and flip between scoring approaches on the same tissue without duplicating the image or cell data - **Pan and zoom on high-resolution morphology images** — OME-TIFF tile pyramid with smooth zoom from whole-tissue to single-cell scale - **Multi-channel morphology** — Xenium `morphology_focus/` channels are selectable alongside the top-level morphology image @@ -24,6 +26,7 @@ Spatial transcriptomics platforms (Xenium, seqFISH, Visium, Visium HD, MERSCOPE, - **Per-panel rotation** — rotate either panel to any angle to align tissue orientation - **Transcript dot overlay** — per-gene colored dots, filterable by gene species, with hover tooltips - **Cell/spot segmentation** — polygon boundaries with color-by-gene-set or color-by-metadata, and editable per-category colors +- **Autocrine signalling** — self-signalling drawn as a ring per cell, colored by the same scale as the directed edges and obeying the same mechanism filter - **Metadata filtering** — restrict the view to a subset of cells or edges (a sample, a few cell types, a value range). Applied server-side before sampling, so a rare cluster renders at full density instead of being sampled away - **Treat-as-categorical toggle** — integer-coded cluster IDs get a discrete editable palette rather than a viridis gradient, with the numeric order preserved in the legend - **Region drawing and measurement tools** — annotate areas, export cell selections, save PNG screenshots diff --git a/backend/app/main.py b/backend/app/main.py index 701e166..0d4f784 100644 --- a/backend/app/main.py +++ b/backend/app/main.py @@ -3,7 +3,7 @@ from app.routers import tiles, spatial, edges, layers -APP_VERSION = "0.9.0" +APP_VERSION = "0.8.2" app = FastAPI(title="TissuePlex API", version=APP_VERSION) diff --git a/backend/app/readers/visium_reader.py b/backend/app/readers/visium_reader.py index f998538..0aa24b1 100644 --- a/backend/app/readers/visium_reader.py +++ b/backend/app/readers/visium_reader.py @@ -25,15 +25,34 @@ ----------------------------------------------------------- 1. **There is no `microns_per_pixel`.** Classic Visium scalefactors carry only the four keys above; 10x deliberately does not record image pixel size, noting that - "prior knowledge of the image pixel sizes is not used". So `pixel_size` is - *derived* from the one physical constant the slide guarantees — a spot is - 55 µm — giving `55 / spot_diameter_fullres` µm per fullres pixel. + "prior knowledge of the image pixel sizes is not used". So `pixel_size` has to + be derived, and *which* constant you derive it from matters a great deal. - 10x cautions that spot diameters are estimates and recommends a calibrated - microscope value instead. That caveat is inherited here: distances in the - measurement tool, and the placement of `edges.parquet` micron coordinates, are - only as good as that estimate. It is reported as `pixel_size_derived: true` in - `info()` so the uncertainty is visible rather than implied. + It is derived from the **100 µm lattice pitch**, measured off the positions + table. Not from `spot_diameter_fullres`, which is the obvious-looking choice + and is wrong by 18%: + + measured on V1_Mouse_Kidney and V1_Adult_Mouse_Brain, independently + in-row pitch 138.00 px (both) + spot_diameter_fullres 89.45 px (both) + ratio 0.6482 (both) + + `spot_diameter_fullres` is Space Ranger's **detected spot footprint**, which at + that ratio is ~64.8 µm — not the 55 µm nominal capture diameter. 10x's own + documentation says as much, describing classic Visium spot diameters as + "approximately 60-70 µm" and cautioning that they are estimates. Deriving from + 55 µm gives 0.615 µm/px where the truth is 0.725, and reconstructs a + 5.4 × 5.7 mm capture area against 10x's specified 6.5 × 6.5 mm. The pitch-based + derivation reconstructs 6.35 × 6.67 mm. + + The pitch is also the better-conditioned measurement: it averages over + thousands of spot positions in a rigid array template, where the diameter is + one estimate of a fuzzy edge. + + `info()` reports `pixel_size_source` — `"lattice_pitch"` normally, or + `"spot_diameter"` when the positions table is too sparse to measure a pitch and + the reader falls back. Distances in the measurement tool and the placement of + `edges.parquet` micron coordinates both inherit whichever was used. 2. **`tissue_positions_list.csv` (Space Ranger < 2.0) has no header row.** Reading it with pandas' default header inference eats the first spot and mislabels @@ -55,11 +74,25 @@ from pathlib import Path from typing import Optional +import numpy as np + +from app.readers.base_reader import _UNSET from app.readers.spaceranger import SpaceRangerReader -# The physical spot diameter, fixed by the slide design. 10x: "Each spot is -# 55 µm in diameter with a 100 µm center to center distance between spots." +# Slide geometry, fixed by design. 10x: "Each spot is 55 µm in diameter with a +# 100 µm center to center distance between spots." SPOT_DIAMETER_UM = 55.0 +SPOT_PITCH_UM = 100.0 + +# What `spot_diameter_fullres` actually corresponds to, in µm. Measured as +# 0.6482 × pitch on both V1_Mouse_Kidney and V1_Adult_Mouse_Brain — i.e. Space +# Ranger's detected spot footprint, roughly 18% wider than the 55 µm capture +# spot. Used only when the pitch cannot be measured; see the module docstring. +DETECTED_SPOT_UM = 64.8 + +# Real Space Ranger writes all 4,992 slide positions. Measuring a pitch from a +# hand-trimmed table with only a handful of spots per row is not trustworthy. +_MIN_PITCH_SAMPLES = 20 # Vertices per spot polygon. Enough that a spot reads as round at the zoom levels # where individual spots are distinguishable, and cheap: a full capture area is @@ -74,6 +107,10 @@ class VisiumReader(SpaceRangerReader): _LOG = "visium" + def __init__(self, dataset_path: Path): + super().__init__(dataset_path) + self._pitch_cache = _UNSET + # ── Space Ranger hooks ──────────────────────────────────────────────────── def _spatial_dir(self) -> Optional[Path]: @@ -87,16 +124,60 @@ def _matrix_path(self) -> Optional[Path]: return p return None + def _lattice_pitch_fullres(self) -> Optional[float]: + """Centre-to-centre spot spacing in full-resolution pixels, or None. + + Visium numbers columns in steps of 2 within a row, with odd rows offset by + half a step, so the in-row spacing between consecutive `array_col` values + two apart *is* the hex nearest-neighbour distance. Measured across every + row and taken as a median, so a few missing spots cannot move it. + """ + if self._pitch_cache is not _UNSET: + return self._pitch_cache # type: ignore[return-value] + self._pitch_cache = None + df = self._read_positions_table() + if df is None or not {"array_row", "array_col", "pxl_col_in_fullres"} <= set(df.columns): + return None + steps: list[float] = [] + for _, g in df.groupby("array_row"): + g = g.sort_values("array_col") + dx = np.diff(g["pxl_col_in_fullres"].to_numpy(dtype=float)) + dc = np.diff(g["array_col"].to_numpy(dtype=float)) + steps.extend(dx[dc == 2].tolist()) + steps = [s for s in steps if np.isfinite(s) and s > 0] + if len(steps) < _MIN_PITCH_SAMPLES: + return None + self._pitch_cache = float(np.median(steps)) + return self._pitch_cache # type: ignore[return-value] + + def pixel_size_source(self) -> str: + """Which constant `pixel_size` was derived from — see the module docstring.""" + if self._lattice_pitch_fullres() is not None: + return "lattice_pitch" + try: + if float(self._scalefactors()["spot_diameter_fullres"]) > 0: + return "spot_diameter" + except (KeyError, TypeError, ValueError): + pass + return "fallback" + def _microns_per_fullres_px(self) -> float: - """Derived from the 55 µm spot spec — classic Visium records no pixel size. + """Derived — classic Visium records no pixel size. + + Preference order, and the reason for it, is in the module docstring: + the 100 µm lattice pitch beats `spot_diameter_fullres`, which measures a + ~64.8 µm detected footprint rather than the 55 µm capture spot. - Falls back to 1.0 rather than raising: an unusable `spot_diameter_fullres` - should degrade the µm labels, not stop the dataset from rendering. + Falls back to 1.0 rather than raising: an unreadable dataset should + degrade the µm labels, not stop the tissue from rendering. """ + pitch = self._lattice_pitch_fullres() + if pitch: + return SPOT_PITCH_UM / pitch try: d = float(self._scalefactors()["spot_diameter_fullres"]) if d > 0: - return SPOT_DIAMETER_UM / d + return DETECTED_SPOT_UM / d except (KeyError, TypeError, ValueError): pass return _FALLBACK_MICRONS_PER_PIXEL @@ -131,16 +212,27 @@ def platform(self) -> str: def info(self) -> dict: sf = self._scalefactors() pos = self._positions() + pitch = self._lattice_pitch_fullres() return { "platform": self.platform, "n_spots": int(len(pos)), - "spot_diameter_um": SPOT_DIAMETER_UM, + # The slide constant, for reference. Note this is NOT what + # spot_diameter_fullres measures — see detected_spot_um below. + "spot_capture_diameter_um": SPOT_DIAMETER_UM, + "spot_pitch_um": SPOT_PITCH_UM, "spot_diameter_fullres": sf.get("spot_diameter_fullres"), "spot_diameter_px": self.unit_diameter_px(), + # What Space Ranger's detected diameter works out to in this dataset. + # ~65 µm on real data, i.e. the printed footprint, not the capture spot. + "detected_spot_um": (None if not sf.get("spot_diameter_fullres") + else float(sf["spot_diameter_fullres"]) + * self._microns_per_fullres_px()), + "lattice_pitch_fullres": pitch, "pixel_size": self.pixel_size, - # Flagged because it is inferred from the spot size rather than - # recorded by the instrument — see the module docstring. + # Always inferred, never recorded by the instrument; this says from + # what. See the module docstring. "pixel_size_derived": True, + "pixel_size_source": self.pixel_size_source(), "tissue_hires_scalef": self.hires_scalef, "tissue_lowres_scalef": sf.get("tissue_lowres_scalef"), } diff --git a/backend/tests/golden_baseline.json b/backend/tests/golden_baseline.json index a40f6a0..156299b 100644 --- a/backend/tests/golden_baseline.json +++ b/backend/tests/golden_baseline.json @@ -1222,7 +1222,7 @@ }, "visium_tiny": { "bounds_full": { - "digest": "394f7bcf07a3f07e", + "digest": "fb08e147389983f1", "keys": [ "cell_id", "vertex_x", @@ -1233,7 +1233,7 @@ "total": 252 }, "bounds_half": { - "digest": "bfe707b219830296", + "digest": "98da9b9c06491a61", "n": 2016, "n_cells": 126, "total": 252 @@ -1387,13 +1387,17 @@ "n": 36 }, "info_keys": [ + "detected_spot_um", + "lattice_pitch_fullres", "n_spots", "pixel_size", "pixel_size_derived", + "pixel_size_source", "platform", + "spot_capture_diameter_um", "spot_diameter_fullres", "spot_diameter_px", - "spot_diameter_um", + "spot_pitch_um", "tissue_hires_scalef", "tissue_lowres_scalef" ], diff --git a/docs/index.html b/docs/index.html index 4d91cab..2a502c6 100644 --- a/docs/index.html +++ b/docs/index.html @@ -205,7 +205,7 @@ 2 · Install Docker Desktop 3 · Download TissuePlex 4 · First launch - 5 · Load your Xenium data + 5 · Load your data

@@ -221,6 +221,7 @@ Interface overview Morphology image Cell Segments + Filtering by metadata Transcripts Tissue Graph Directed Edges @@ -252,8 +253,12 @@

TissuePlex User Manual

machine.

No programming experience is required. This guide covers installation, data setup, and every control in the interface.

+

Six platforms are supported — Xenium, Visium, Visium HD, MERSCOPE, CosMx and + seqFISH — and TissuePlex works out which one it is looking at from the files in + the folder. The examples below use Xenium; everything transfers, and the + platform-specific differences are called out where they matter.

By the end of this guide you will have: TissuePlex installed - and running, a Xenium dataset loaded, cell-cell communication edges from NICHESv2 + and running, your dataset loaded, cell-cell communication edges from NICHESv2 visible, and custom analysis results available as cell color overlays.

@@ -267,10 +272,12 @@

A Mac or Windows computer

TissuePlex runs in the browser. No special graphics card is needed.

-

Your Xenium output folder

-

The folder produced by the Xenium instrument, containing - experiment.xenium, morphology.ome.tif, - transcripts.parquet, and related files.

+

Your platform's output folder

+

Whatever your instrument or pipeline produced, unmodified. For Xenium that + is the folder containing experiment.xenium, + morphology.ome.tif and transcripts.parquet; for the + Visium platforms it is the contents of Space Ranger's outs/. See + the platform table in step 5.

An internet connection

@@ -451,7 +458,7 @@

Stopping TissuePlex

5
-

Load Your Xenium Dataset

+

Load Your Dataset

TissuePlex reads from a parent folder containing one or more dataset subfolders. The expected layout:

@@ -481,13 +488,30 @@

Windows (Git Bash)

TissuePlex detects the platform from the files present in each subfolder:

- - - - + + + + + + +
PlatformRequired file in the dataset folder
Xenium (10x Genomics)experiment.xenium
MERSCOPE (Vizgen)cell_by_gene.csv or cell_metadata.csv
CosMx (Nanostring)a file ending in _tx_file.csv
PlatformRequired file in the dataset folderUnit
Xenium (10x Genomics)experiment.xeniumcell
Visium HD (10x Genomics)binned_outputs/square_*um/bin
Visium (10x Genomics)spatial/scalefactors_json.json plus a tissue_positions filespot
MERSCOPE (Vizgen)cell_by_gene.csv or cell_metadata.csvcell
CosMx (Nanostring)a file ending in _tx_file.csvcell
seqFISH (Spatial Genomics)a file matching *_CellCoordinates*.csvcell

Folders without a matching sentinel file are silently skipped.

+ +
+ Point at the right folder. For the 10x array + platforms, the dataset folder is the contents of Space Ranger's + outs/ directory — the folder that directly contains + spatial/ — not its parent. +
+ +

The interface adapts to whichever platform is loaded. Panels are relabelled + to match the unit — a Visium dataset says Spot Segments, Spot + Filter and Spot Info where a Xenium one says Cell, and + Visium HD says Bin. Layers a platform cannot serve are hidden + entirely: the two Visium platforms measure counts per spot or bin rather than + per molecule, so the Transcripts layer does not appear for them.

@@ -816,8 +840,83 @@

Cell Metadata

use a gradient palette with clamp controls.
  • Categorical columns (cluster labels, cell type names, etc.) are assigned distinct colors automatically, one per category, with a - legend.
    • + legend of editable color swatches. + + +

    Treat as Categorical

    +

    When you pick a numeric column, a treat as categorical + checkbox appears beneath the dropdown. This is the control you want for + integer-coded categories.

    +

    Cluster IDs are the usual case: fwrite on a Seurat + @meta.data writes seurat_clusters as plain integers, + so nothing in the file distinguishes "cluster 7" from a measurement. TissuePlex + guesses — an integer column with 30 or fewer distinct values is treated as + categorical — and the checkbox overrides that guess in either direction:

    +
      +
    • Checked — one distinct, editable color per value, with the + legend in numeric order, so cluster 10 comes after cluster 2 rather + than between 1 and 2.
      • +
    • Unchecked — a continuous gradient with clamp sliders. Useful + for a coded column with few levels that you would rather read as a scale.
      • +
    • An auto button returns the column to automatic + detection.
      • +
    +

    The choice is remembered per column for the session. It applies to edge + metadata too, in the Edge Data section.

    + + + + +
    +

    Filtering by Metadata

    +

    The Cell Filter section restricts the whole view to a + subset of your data — one sample, a few cell types, a range of values. Everything + outside the subset is simply not drawn.

    + +

    How to use it

    +
      +
    • Pick a column from the dropdown. Any cell metadata column works, including + supplemental ones.
      • +
    • Categorical columns give you a checkbox per value. Tick the + ones you want; all and none + buttons are provided. With nothing ticked, everything is shown — an empty + selection means "no restriction", not "hide everything".
      • +
    • Continuous columns give you min and + max boxes, both optional and both inclusive. The + placeholder text shows the column's actual range.
      • +
    • clear filter removes the restriction.
      • +
    + +

    What the filter affects

    +
      +
    • Cell segments — only matching cells are drawn, and only + they can be clicked or captured by a region selection.
      • +
    • Edges and the tissue graph — an edge is drawn only when + both of its endpoints match. The point of narrowing to two or three + cell types is the signalling within that subset, and an edge with one endpoint + outside would run off to a cell that is not on screen.
      • +
    • Transcripts are not filtered. Several platforms ship no + transcript-to-cell assignment at all, so there is nothing to match on.
    + +
    + Why the subset stays dense. The filter is applied + on the server, before sampling. If it were applied afterwards you would see a + sample of a subset — narrowing to a cluster holding 5% of cells while the + sampler is at 10% would draw about 0.5% of the tissue. Filtering first means the + subset renders at full density, and the shown/total counter beside + Cell Segments updates to describe the subset. +
    + +

    Filtering edges

    +

    The Edge Filter inside Edge + Data works the same way but on columns of the edge table itself, including + annotations from an edge-metadata/ folder — a curation call, a + confidence score, a review flag. It composes with the cell filter: set both and + you see only the annotated edges among your chosen cells.

    +

    This is distinct from the LRM Mechanisms checklist + below it. The checklist chooses which mechanisms are scored on every + edge; the edge filter chooses which cell pairs are drawn at all.

    @@ -923,13 +1022,37 @@

    Arrowheads

    Autocrine

    -

    The Autocrine checkbox toggles visibility of - autocrine signals (cases where a cell signals to itself), rendered as rings. - When on, two sliders appear:

    +

    The Autocrine checkbox draws autocrine signalling — + a cell signalling to itself — as a ring around that cell. NICHESv2 emits one + autocrine edge per cell, so on real output expect a ring on every cell that has + any self-signal.

    +

    Autocrine rings behave exactly like directed edges in every other respect:

    +
      +
    • They are colored by the same control and the same scale. + Set edge color to LRM Set and the rings are + shaded by signal strength alongside the lines; set it to a + metadata column and they take that column's colors. One legend + describes everything on screen.
      • +
    • They obey the LRM Mechanisms checklist. Narrow to one + mechanism and only the cells with autocrine signal through that mechanism keep + their ring.
      • +
    • They obey the cell and edge metadata filters, and the density slider.
      • +
    • Clicking a ring opens the same edge detail panel, listing the mechanisms + scored for that self-edge.
      • +
    +

    Two sliders appear when the checkbox is on:

      -
    • ring r: ring radius.
      • +
    • ring r: ring radius, in image pixels. Increase it + on large tissues where the default is small relative to the field of view.
    • ring w: ring line width.
    +
    + Seeing nothing? The + Edges layer must be on for rings to appear — + Autocrine lives inside it. Also check the + density slider: at the default 10% only a tenth of + edges, rings included, are drawn. +

    Width and Offset

      diff --git a/docs/public_datasets.md b/docs/public_datasets.md index d661509..cebaaf7 100644 --- a/docs/public_datasets.md +++ b/docs/public_datasets.md @@ -1,6 +1,49 @@ -# Public Xenium Datasets for Development & Testing +# Public Datasets for Development & Testing -This document lists publicly available 10x Genomics Xenium datasets suitable for developing and testing ConnectivityExplorer across diverse tissue types, species, and data configurations. +This document lists publicly available datasets suitable for developing and testing +TissuePlex across platforms, tissue types, species, and data configurations. Most of it +covers 10x Xenium; see the classic Visium section at the end for the datasets the Visium +reader was verified against. + +--- + +## Classic Visium — reader verification set + +The two datasets the Visium reader was validated on. Both are Space Ranger 1.1.0, which +means they carry the **headerless** `tissue_positions_list.csv` — and that is the common +case, because every classic Visium dataset 10x serves without registration predates the +Space Ranger 2.0 rename. The CytAssist-era 2.x downloads return HTTP 403. + +You only need two files per dataset, not the full `outs/`: + +```bash +B=https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Mouse_Kidney +mkdir -p sample_data/visium_mouse_kidney && cd sample_data/visium_mouse_kidney +curl -LO $B/V1_Mouse_Kidney_filtered_feature_bc_matrix.h5 +curl -LO $B/V1_Mouse_Kidney_spatial.tar.gz +mv V1_Mouse_Kidney_filtered_feature_bc_matrix.h5 filtered_feature_bc_matrix.h5 +tar xzf V1_Mouse_Kidney_spatial.tar.gz && rm V1_Mouse_Kidney_spatial.tar.gz +``` + +| Dataset | Size | Spots in tissue | Notes | +|---|---|---|---| +| `V1_Mouse_Kidney` | 12 MB + 8 MB | 1,438 of 4,992 | The verification dataset. `visium_mouse_*/` is gitignored. | +| `V1_Adult_Mouse_Brain` | 21 MB + 9 MB | — | Used only to cross-check the lattice geometry. | + +**What they established**, none of which the synthetic fixture could: + +- `tissue_hires_scalef` is 0.177 and 0.170 — nowhere near the 1.0 that hides a missing + coordinate multiply. +- The in-row lattice pitch is exactly 138.00 fullres px on both, while + `spot_diameter_fullres` is 89.45 — a ratio of 0.648, proving that field measures a + ~64.8 µm detected footprint rather than the 55 µm capture spot. See the Visium section + in CLAUDE.md; deriving `pixel_size` from 55 µm is wrong by 18%. +- Registration: 98.1% of `in_tissue` spots land on stained tissue and 99.5% of + out-of-tissue spots land on bare slide, measured as mean inverse intensity under each + spot polygon in the hires PNG. +- NICHESv2 end to end on the kidney: 1,438 spots, 9,666 edges, 2,479 LRMs, 3.2M rows. Top + mechanisms include `Apoe|Lrp2` and `Cst3|Lrp2` — megalin-mediated proximal tubule + uptake, which is the expected kidney biology. --- diff --git a/frontend/package.json b/frontend/package.json index 28e6b20..999c015 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -1,6 +1,6 @@ { "name": "tissueplex", - "version": "0.9.0", + "version": "0.8.2", "private": true, "scripts": { "dev": "vite", diff --git a/frontend/src/components/Viewer.jsx b/frontend/src/components/Viewer.jsx index 3e3ec55..7d9aaf4 100644 --- a/frontend/src/components/Viewer.jsx +++ b/frontend/src/components/Viewer.jsx @@ -695,10 +695,15 @@ function ViewerPanel({ panelIndex }) { return result; }, [edges]); + // Both layers apply the LRM filter identically. Autocrine used to skip the + // visible_lrm_count test, so hiding every mechanism removed the directed edges + // and left a ring on every cell — which reads as the autocrine layer ignoring + // the controls entirely. + const hasVisibleLrms = (r) => (r.visible_lrm_count ?? r.lrm_count ?? 0) > 0; const { directedEdges, autocrineCells } = useMemo(() => ({ - directedEdges: edges.filter((r) => !r.is_autocrine && (r.visible_lrm_count ?? r.lrm_count ?? 0) > 0), + directedEdges: edges.filter((r) => !r.is_autocrine && hasVisibleLrms(r)), autocrineCells: edges - .filter((r) => r.is_autocrine) + .filter((r) => r.is_autocrine && hasVisibleLrms(r)) .map((r) => ({ ...r, x: r.x1, y: r.y1 })), }), [edges]); diff --git a/frontend/src/hooks/useEdgeColors.js b/frontend/src/hooks/useEdgeColors.js index 618fcb4..6a0e9ba 100644 --- a/frontend/src/hooks/useEdgeColors.js +++ b/frontend/src/hooks/useEdgeColors.js @@ -46,14 +46,19 @@ export function useEdgeColors( const [rawMeta, setRawMeta] = useState(null); // ── lrm_set: compute synchronously from edges.visible_score_sum ────────── + // + // Autocrine edges are included. They used to be filtered out here, which left + // the autocrine rings permanently on their default orange no matter what the + // colour control said — the one edge type that ignored "colour by LRM set". + // A shared scale is safe: measured across the bundled datasets, autocrine + // score_sum medians run 1.00–1.33× the directed medians, so they neither + // dominate the range nor need a scale of their own. One scale, one legend, + // and every edge on screen is described by it. const lrmSetResult = useMemo(() => { if (!enabled || edgeColorBy?.mode !== "lrm_set" || !edges || edges.length === 0) { return null; } - const directed = edges.filter((e) => !e.is_autocrine); - if (directed.length === 0) return null; - - const entries = directed.map((e) => [e.edge, e.visible_score_sum ?? 0]); + const entries = edges.map((e) => [e.edge, e.visible_score_sum ?? 0]); const scores = entries.map(([, v]) => v); let min = Infinity, max = -Infinity; for (const v of scores) { if (v < min) min = v; if (v > max) max = v; } diff --git a/r/niches_visium.R b/r/niches_visium.R index df9163a..d811590 100644 --- a/r/niches_visium.R +++ b/r/niches_visium.R @@ -16,14 +16,20 @@ ## The one thing classic Visium does differently, and it matters ## -------------------------------------------------------------- ## There is no `microns_per_pixel` in scalefactors_json.json. 10x does not record -## the image pixel size for classic Visium. So the conversion has to come from the -## one physical constant the slide guarantees: +## the image pixel size for classic Visium, so the conversion must be derived — +## and the obvious-looking derivation is wrong by 18%. ## -## a Visium spot is 55 um across -## => microns_per_pixel = 55 / spot_diameter_fullres +## DON'T: 55 / spot_diameter_fullres +## DO: 100 / ## -## 10x notes that spot diameters are estimates and recommends a calibrated -## microscope value where you have one. Pass --mpp to override. +## `spot_diameter_fullres` is Space Ranger's DETECTED spot footprint, not the +## 55 um capture spot. Measured on V1_Mouse_Kidney and V1_Adult_Mouse_Brain it is +## 0.6482 x the lattice pitch, i.e. ~64.8 um — which is why 10x's own docs +## describe classic Visium spot diameters as "approximately 60-70 um" and warn +## that they are estimates. The 100 um centre-to-centre pitch is a hard geometric +## constant and averages over thousands of positions, so that is what we use. +## +## Pass --mpp to override with a calibrated microscope value. ## ## Spots are not cells. A 55 um spot holds roughly 1-10 cells, so an edge between ## two spots is a neighbourhood statistic, not a cell-cell claim. Say so in @@ -49,8 +55,9 @@ source(file.path(if (is.na(.this)) "r" else dirname(.this), "niches_common.R")) # The slide spec: "Each spot is 55 um in diameter with a 100 um center to center # distance between spots." -SPOT_DIAMETER_UM <- 55 -SPOT_PITCH_UM <- 100 +SPOT_DIAMETER_UM <- 55 # capture spot +SPOT_PITCH_UM <- 100 # centre-to-centre; the constant we actually derive from +DETECTED_SPOT_UM <- 64.8 # what spot_diameter_fullres measures — see above # ── Parameters ──────────────────────────────────────────────────────────────── args <- commandArgs(trailingOnly = TRUE) @@ -104,8 +111,9 @@ if (file.exists(pos.modern)) { stop("no tissue_positions.csv or tissue_positions_list.csv in ", file.path(DATASET, "spatial")) } +pos.all <- pos # pitch is measured over the whole array pos <- pos[pos$in_tissue == 1, , drop = FALSE] -cat(sprintf(" %d in-tissue spots\n", nrow(pos))) +cat(sprintf(" %d in-tissue spots (of %d on the slide)\n", nrow(pos), nrow(pos.all))) # ── 3. The conversion: fullres pixels -> microns ───────────────────────────── sf.path <- file.path(DATASET, "spatial", "scalefactors_json.json") @@ -114,15 +122,35 @@ if (is.finite(MPP_ARG) && MPP_ARG > 0) { mpp <- MPP_ARG cat(sprintf(" microns_per_pixel: %.6f (supplied via --mpp)\n", mpp)) } else { - spot.px <- as.numeric(sf$spot_diameter_fullres) - if (!is.finite(spot.px) || spot.px <= 0) - stop("spot_diameter_fullres missing or invalid in ", sf.path, - " — pass --mpp instead") - mpp <- SPOT_DIAMETER_UM / spot.px - cat(sprintf(" microns_per_pixel: %.6f (DERIVED from %.1f um / %.1f px)\n", - mpp, SPOT_DIAMETER_UM, spot.px)) - cat(" note: 10x calls spot diameters estimates; pass --mpp if you have a\n") - cat(" calibrated microscope value.\n") + # Measure the in-row lattice pitch: within a row, array_col steps by 2 and the + # spacing between those spots IS the hex nearest-neighbour distance. Median over + # every row, so missing spots cannot move it. + steps <- unlist(lapply(split(pos.all, pos.all$array_row), function(g) { + g <- g[order(g$array_col), ] + dx <- diff(g$pxl_col_in_fullres); dc <- diff(g$array_col) + dx[dc == 2] + }), use.names = FALSE) + steps <- steps[is.finite(steps) & steps > 0] + if (length(steps) >= 20) { + pitch.px <- stats::median(steps) + mpp <- SPOT_PITCH_UM / pitch.px + cat(sprintf(" microns_per_pixel: %.6f (from %.0f um pitch / %.2f px)\n", + mpp, SPOT_PITCH_UM, pitch.px)) + spot.px <- as.numeric(sf$spot_diameter_fullres) + if (is.finite(spot.px)) + cat(sprintf(" (spot_diameter_fullres %.1f px = %.1f um detected, not the %.0f um\n" + , spot.px, spot.px * mpp, SPOT_DIAMETER_UM), + " capture spot — do not derive from it)\n", sep = "") + } else { + spot.px <- as.numeric(sf$spot_diameter_fullres) + if (!is.finite(spot.px) || spot.px <= 0) + stop("cannot measure the lattice pitch and spot_diameter_fullres is invalid in ", + sf.path, " — pass --mpp instead") + mpp <- DETECTED_SPOT_UM / spot.px + cat(sprintf(" microns_per_pixel: %.6f (FALLBACK: %.1f um detected / %.1f px)\n", + mpp, DETECTED_SPOT_UM, spot.px)) + cat(" warning: too few positions to measure the lattice pitch.\n") + } } meta.data <- data.frame( diff --git a/sample_data/.gitignore b/sample_data/.gitignore index e129bd6..18e5b01 100644 --- a/sample_data/.gitignore +++ b/sample_data/.gitignore @@ -4,6 +4,10 @@ xenium_human_breast_2fov/ merscope-*/ cosmx-*/ visium-*/ +# Real 10x Visium downloads. visium_tiny/ is the committed synthetic fixture and +# is deliberately NOT matched by this pattern. +visium_mouse_*/ +visium_human_*/ # seqFISH / Spatial Genomics GenePS datasets. # The scverse test dataset (s3.embl.de/spatialdata/raw_data/seqfish-2-test-dataset.zip) diff --git a/sample_data/make_visium.py b/sample_data/make_visium.py index 5c64067..7aced3f 100644 --- a/sample_data/make_visium.py +++ b/sample_data/make_visium.py @@ -17,8 +17,14 @@ why a missing multiply went unnoticed there for a whole release. This fixture uses 0.08 — the realistic order of magnitude — so the same bug fails loudly. * **No ``microns_per_pixel`` key.** Classic Visium scalefactors carry only the - four keys 10x actually writes, forcing ``pixel_size`` down the derived path - (``55 µm / spot_diameter_fullres``) rather than the recorded one. + four keys 10x actually writes, forcing ``pixel_size`` down a derived path. + * **``spot_diameter_fullres`` is the DETECTED footprint, not the 55 µm capture + spot.** Measured at 0.6482 × the lattice pitch on both V1_Mouse_Kidney and + V1_Adult_Mouse_Brain, i.e. ~64.8 µm. The first version of this generator set + it to exactly ``55 / microns_per_pixel``, which made the fixture a tautology: + it confirmed whatever derivation the reader used. It now emits the real + ratio, so deriving from 55 µm instead of the pitch produces a pixel size 18% + too small and the lattice check in ``r/niches_visium.R`` fails. * **A hexagonal lattice with the odd-row offset**, not a square grid, and with off-tissue spots present and flagged ``in_tissue = 0`` so the filter is exercised. @@ -41,9 +47,12 @@ import numpy as np -# Slide geometry, fixed by 10x: 55 µm spots on a 100 µm centre-to-centre pitch. +# Slide geometry, fixed by 10x: 55 µm capture spots on a 100 µm centre-to-centre +# pitch. DETECTED_SPOT_UM is what Space Ranger's `spot_diameter_fullres` actually +# measures — the printed footprint, ~18% wider — as observed on two real datasets. SPOT_DIAMETER_UM = 55.0 SPOT_PITCH_UM = 100.0 +DETECTED_SPOT_UM = 64.8 # Deliberately far from 1.0 — see the module docstring. HIRES_SCALEF = 0.08 @@ -180,7 +189,8 @@ def main() -> int: positions = build_positions(args.rows, args.cols, rng) in_tissue = [p for p in positions if p["in_tissue"] == 1] - spot_diameter_fullres = SPOT_DIAMETER_UM / MICRONS_PER_FULLRES_PX + # Deliberately the detected footprint, not 55 µm — see the module docstring. + spot_diameter_fullres = DETECTED_SPOT_UM / MICRONS_PER_FULLRES_PX # Image is the fullres frame scaled by HIRES_SCALEF. max_x = max(p["pxl_col_in_fullres"] for p in positions) @@ -243,8 +253,14 @@ def main() -> int: print(f"wrote {out}/") print(f" {len(positions)} spots ({len(in_tissue)} in tissue), {len(genes)} genes") print(f" hires image {hires_w}x{hires_h} px (tissue_hires_scalef={HIRES_SCALEF})") + pitch_px = SPOT_PITCH_UM / MICRONS_PER_FULLRES_PX + print(f" lattice pitch={pitch_px:.1f} px -> correct um/px={SPOT_PITCH_UM / pitch_px:.4f}") print(f" spot_diameter_fullres={spot_diameter_fullres:.2f} px " - f"-> derived pixel_size={MICRONS_PER_FULLRES_PX / HIRES_SCALEF:.4f} um/hires-px") + f"(detected {DETECTED_SPOT_UM} um, NOT the {SPOT_DIAMETER_UM} um capture spot)") + print(f" deriving from {SPOT_DIAMETER_UM} um would give " + f"{SPOT_DIAMETER_UM / spot_diameter_fullres:.4f} um/px — 18% wrong, and that is " + f"the regression this fixture exists to catch") + print(f" derived pixel_size={MICRONS_PER_FULLRES_PX / HIRES_SCALEF:.4f} um/hires-px") return 0 diff --git a/sample_data/visium_tiny/PROVENANCE.md b/sample_data/visium_tiny/PROVENANCE.md index f23c2ab..1c8fb78 100644 --- a/sample_data/visium_tiny/PROVENANCE.md +++ b/sample_data/visium_tiny/PROVENANCE.md @@ -29,12 +29,29 @@ reader docstring claimed otherwise. The check that catches it: | with the multiply | 252 / 252 | | without it | 0 / 252 | -It also forces the *derived* pixel-size path, because a real classic Visium -`scalefactors_json.json` carries no `microns_per_pixel` — only the four keys 10x -actually writes. The generator uses 0.25 µm per fullres pixel and a 220 px spot -diameter; `55 / 220 = 0.25` recovers it exactly, and the NICHESv2 script's -independent check on the lattice reports a median nearest-neighbour distance of -100.0 µm, which is the Visium pitch. +### `spot_diameter_fullres` is the detected footprint, not the capture spot + +The second thing this fixture exists for, and it was learned the hard way. + +A real `scalefactors_json.json` carries no `microns_per_pixel`, so `pixel_size` must +be derived. The first version of this generator set +`spot_diameter_fullres = 55 µm / microns_per_pixel`, which made the fixture a +**tautology**: whatever derivation the reader used, the fixture agreed with it. The +reader derived from the 55 µm capture spot and the fixture happily confirmed it. + +Real data says otherwise. On `V1_Mouse_Kidney` and `V1_Adult_Mouse_Brain` the in-row +lattice pitch is exactly 138.00 fullres px while `spot_diameter_fullres` is 89.45 — a +ratio of 0.648, so that field measures a ~64.8 µm *detected* footprint, not the 55 µm +capture spot. Deriving from 55 µm is 18% wrong. + +The generator now emits the real ratio (259.2 px for a 0.25 µm/px scan), so: + +| derivation | pixel_size | measured nearest-neighbour | +|---|---|---| +| 100 µm lattice pitch (correct) | 3.125 | **100.0 µm** | +| 55 µm capture spot (the bug) | 2.652 | 84.9 µm | + +which is the same failure signature the real datasets show (99.3 vs 84.2 µm). ## What it is not @@ -43,6 +60,7 @@ not.** Counts are Poisson noise with three arbitrary x-axis domains over a 36-ge panel chosen to form complete connectomedb2025 ligand-receptor pairs, so NICHESv2 has something to score. Nothing here should be read as a result. -No real Space Ranger `outs/` tree has been through this reader yet. A passing render -here is necessary, not sufficient — the same caveat `visium_hd_tiny/PROVENANCE.md` -makes, for the same reason. +The reader has since been verified against real Space Ranger output — see the classic +Visium section of `docs/public_datasets.md`. That verification is what corrected the +pixel-size derivation above, which is the standing argument for not trusting a +synthetic fixture on its own: it can only test what you already believed. diff --git a/sample_data/visium_tiny/spatial/scalefactors_json.json b/sample_data/visium_tiny/spatial/scalefactors_json.json index 32c260f..6ef0b21 100644 --- a/sample_data/visium_tiny/spatial/scalefactors_json.json +++ b/sample_data/visium_tiny/spatial/scalefactors_json.json @@ -1,6 +1,6 @@ { - "spot_diameter_fullres": 220.0, - "fiducial_diameter_fullres": 550.0, + "spot_diameter_fullres": 259.2, + "fiducial_diameter_fullres": 648.0, "tissue_hires_scalef": 0.08, "tissue_lowres_scalef": 0.024 } diff --git a/sample_data/visium_tiny/spatial/tissue_hires_image.png b/sample_data/visium_tiny/spatial/tissue_hires_image.png index fd8678112db7497a161e03893bd9ab1ed346230d..7afe97ce9953fc7786121b7b96ee344810beb365 100644 GIT binary patch literal 6568 zcmeHMc|6qX8vjjG)WMxP?zvixa>@~(Od&d$(WXdR70Omeqm7Cd>r4@+b=os!J+?}j zgi|t%D1?kGO+`A6%vcYZ8O$`x{cZSBx!uqG=l*g2^OAOrJIjMcfG4WsB3X;Le&n{n2Wdv zOBa``Ow-Zp4Vza{;@`mBl2&?dy!D?Muimeq?wIc|b7p5**-iG$k}d)OOdi;JXbJ%6 zSm6O{95)sR=*)%wv`^ zY+0VAI4j1Lf~U{18HflI3+@rDTMx~AC~~1s3l0fA`oi&*doCI1hx??Lukz)-p+l{8 z&-Es2VrO5-_Kq}$f_apNOAhXy=GMmZ%V?UmJ4F1F(x+8*tv|NOOWUTtXF9YfgF{cf z7~5pp>lD1edYgNQh#{XK(y=O1yP~p#m&ORa@9y$KHU5%Foy&KAAg%R3sn`&=#T6H= zi|y%kEipIgT@?~2Qdz$Od->s3KX#{|@i4vL1v$hYWrYZ;MgK8#PPw!_k=AJx zPejRqZQa)FIU)d7(^_@0e%E+;BH1(s$U3y-{I<+z^b`)O{=@dHd}%B3lbZMHz=+wh z(nl}i^qi4XOlCuImIaMXF}wjgoj-ZDwW$%{?P5Nuru^~7ILlw)9WcN_qRkct)13#h z|LL9B`oa9;g2%5b_AbdN=rmMB~flMBWqLB~Czmlx;ohR(xmZ?)rj_XGIed+Q{CF-oAz@yP@i z5Y5sjp$mx~U_)gstmK3vw=m@e({r|;Z|-ZS;0;?kgt;9#JBH#*fni;CbH}#TmD|V{sXbOv_3n#mQZ3)p~MR2j~`ijA|jY3TuwE1=|GG+WU zn8iiJ;^;Wi#4%*Wn2CX&7y0ZGisxihfcSoYO<5UwU0k(t0*i$Wz|Rn&V$>CCYcwm;`<*W1Zg#Fu~W{Ice`n zHb|Fxk)z_QDz*BWUTCQFlHNw}oM6O5A9F3;U!N9L{M4Iq*rxz#1a$QgT`S}8>f?mLFTkF>Pwh}=Kk?&&3Cvd*3 z*G#P-<*|-H7JBb6$(!b2&ut^NYRsYOl%v2c4<=E9qK!Ij19weKPAn6*ClWiggikDI zHnBaHR~skx7&?d8Su9n8XX}Ay@2;bC+63;>$xlci=U?3T9^ZFe+8QT_Guqbz(&bj= zv_G_vEUXrluH(7~mB`H}MoMx#YX3YOrT1e~6dXia<6Z%4>Z4-n#GQMS89NI)?>VbQ67D zy9YasM2Rsnv`0FKo#@Ai6y^>Bw_=?@Ee)-m?;X*{`=1A9An+p$r!sHAcaP85NkApA`c{EIb;Hf`a& z2t==l@=axmjYOMy2mz-w&v1F3hu!DY7^!8dHc}cNH}1uhKk8S+#WE=|+62MT;RdVJ z+6BvvU0_Q2pmSM2}-X;aN4(20l$doRR3Bp*Wa- z|0+_4Kg9WDPOf6_2-s>MDbK-;HKahWRW^Tmfq`w$C%pQz{@C3I(#@g|VuQUH;1_o7 ztBZWd8Xi{Wj)i?K9j7-l(pr3Tq^V14-1!>i&q~8N!TC8?(exZA09Rbhth2 z-nh3Hm11Y6fC4iUkVVX37V+ye5_>wK@E1F5>^MP%d>rq(n!$rfOP zF+|Vs53uYUl8T>=+PSM#ezdyW3pUo3BPT$sbOXwEB5i6-i@6U=VN7CLaESFEMiPXl zEjR1=qZdm3GSnKtInaT{x-n?XnecJ()(3_hhu_Fj!PZcD2`CmZMzl0Kv7wPk<>uyU z#t;=OY_{JGWM2vSVJ=6L_WXC$bU^=dwId?_CsGf=te7CnXw(j(DsmO+iGWSX zj7V41u~jS=tdvVi$0Z3u0yLLn#qTBMZWE$%$1+&l?sQr*_Kt?jztvsv2uqGiljmaZ zuy4ykMjz@~})G$uuv4R=Y|mx|!Ffg$zf71IsYy zoy()K59c962gF&O2lK60BX$BNcxGRv9Pd2jnPHcAJRFK?q=$oI3jFw)hl9dj^_hn| zPy&)jzv8I(2Po<5hcA3`SFH6ng8Tk?A~DiOF=-EqZfG>A73NXFN$kwiUmk@3zC{}$yc&kD(;{kGhF3I2aPb_)@O$#;mm_7_E#I5 zn3D!0_RvHXDyrFi8PWvPY2<=`plPb=oU-N+%AjeLs;|SpY#iVrwTZ$W1}Zd-?^}kV zbia1S2h^Blw+GW_p$0GEN!Y&zCHD1GM6?PXX)Db4w@|#%NgrGeT8sm|XLlb60?o%K?RC&^Igw3Z^>2Z*$$0hM1A!AMRpFZ-+vyzaBfBYsdcIz zGxPh^?XTUOp|Cj%Ki3f&8vqqC$>k)^iN=)z{&TGGx@9|Sa_C<2><)YtL!_>rYC_6! 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Previously six of nine had edges and exactly one had annotations. Two new NICHESv2 scripts complete the set — one per platform r/niches_merscope.R The easy case for coordinates: cell_metadata.csv is already microns. Two traps instead: EntityID is a 19-digit integer that R silently mangles if read as numeric, and small targeted panels usually carry no complete LR pair. r/niches_cosmx.R Cell identity is the (fov, cell_ID) pair, and coordinates are slide-frame PIXELS that must be shifted to the reader's origin (min - 50) and THEN scaled to microns. Skip the shift and edges land tens of thousands of pixels away; skip the scale and they are off by ~8x. Results: CosMx 169,219 edges across 228 LRMs, top mechanisms App|Aplp2 and Prnp|Ncam1 — neural adhesion, as expected for mouse brain. MERSCOPE 14,503 edges but only 2 LRMs, because that 130-gene human panel contains just THBS1->LRP1 and CEMIP->LRP1. Why targeted panels often cannot be scored seqfish_instrument2 has 12 genes and ZERO complete ligand-receptor pairs, so no real run is possible. check_lr_coverage() catches that in a second rather than ten minutes. It gets synthetic edges from make_edges.py instead, which is now platform-agnostic: it reads centroids through the reader and converts image pixels back to microns, so it works on any of the six platforms rather than only Xenium. Fixes a Visium HD bin mismatch that shipped in v0.7.0 niches_visium_hd.R defaulted to square_016um while visium_hd_reader served square_008um. Barcodes are bin-size specific, so the fixture had ZERO ids in common between its edges and its bins. The edges still drew, because they carry their own coordinates, but nothing joined: clicking a bin found no edge, the metadata filter dropped every edge, and the tissue graph floated free of the bins beneath it. _bin_from_edges() now reads one barcode from the edge file and serves the matching bin, falling back to Space Ranger's default when a dataset has no edges. That is also right on the merits — 8 um bins are usually too sparse to score (233,531 edges of which 83 scored, against 66,001 edges across 69 LRMs at 16 um), and whoever ran NICHESv2 already made that call. Verified: all nine datasets now have 100% barcode overlap between edges and units, with a median endpoint-to-centroid offset of 0.000 px. sample_data/make_edge_metadata.py Derives interaction_class, score_tier and score_percentile from the edge file itself, plus one clearly-named invented demo_review_flag so the categorical filter has something to bite on. score_percentile is continuous on purpose — every other column is categorical, and the gradient path needs exercising too. Each folder gets a README naming which columns are derived and which is fabricated. A CSV of plausible-looking annotations sitting next to real kidney or brain data is a hazard; someone will eventually read "score_tier: high" as a result. None of it is analysis output. Also: the four LayerPanel sections that fetch per-dataset endpoints now wait for `dataset` to resolve. They were firing five 404s against /spatial/null/... on every cold load, which is noise that buries real errors. v0.8.3. Guard: 238 probes across 8 datasets. Co-Authored-By: Claude Opus 5 --- CLAUDE.md | 34 +- README.md | 2 + backend/app/main.py | 2 +- backend/app/readers/visium_hd_reader.py | 61 +- backend/tests/golden_baseline.json | 376 +- frontend/package.json | 2 +- frontend/src/components/LayerPanel.jsx | 6 + r/README.md | 14 +- r/niches_cosmx.R | 191 + r/niches_merscope.R | 142 + r/niches_visium_hd.R | 23 +- sample_data/make_edge_metadata.py | 211 + sample_data/make_edges.py | 36 +- .../mouse_ileum_tiny/edge-metadata/README.md | 23 + .../edge-metadata/example_annotations.csv | 532 +- .../seqfish_synthetic/edge-metadata/README.md | 23 + .../edge-metadata/example_annotations.csv | 251 + .../visium_hd_tiny/edge-metadata/README.md | 23 + .../edge-metadata/example_annotations.csv | 66002 ++++++++++++++++ .../visium_tiny/edge-metadata/README.md | 23 + .../edge-metadata/example_annotations.csv | 1643 + 21 files changed, 69313 insertions(+), 307 deletions(-) create mode 100644 r/niches_cosmx.R create mode 100644 r/niches_merscope.R create mode 100644 sample_data/make_edge_metadata.py create mode 100644 sample_data/mouse_ileum_tiny/edge-metadata/README.md create mode 100644 sample_data/seqfish_synthetic/edge-metadata/README.md create mode 100644 sample_data/seqfish_synthetic/edge-metadata/example_annotations.csv create mode 100644 sample_data/visium_hd_tiny/edge-metadata/README.md create mode 100644 sample_data/visium_hd_tiny/edge-metadata/example_annotations.csv create mode 100644 sample_data/visium_tiny/edge-metadata/README.md create mode 100644 sample_data/visium_tiny/edge-metadata/example_annotations.csv diff --git a/CLAUDE.md b/CLAUDE.md index 70bc2d3..aadf61a 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -140,7 +140,7 @@ backend/ Dockerfile tests/ golden_snapshot.py Reader regression guard — see Development Workflow - golden_baseline.json Recorded baseline (217 probes / 8 datasets) + golden_baseline.json Recorded baseline (238 probes / 8 datasets) frontend/ src/ @@ -182,12 +182,19 @@ sample_data/ Partially gitignored — default data mount for loc make_seqfish.py Synthetic seqFISH v2 ROI generator (committable fixture) make_visium.py Synthetic classic Visium generator (committable fixture); non-identity tissue_hires_scalef on purpose — see below + make_edge_metadata.py Demo edge-metadata/ for any dataset with edges. Three + columns derived from the edge file, one clearly-named + invented flag, plus a README in each folder saying which + is which. Nothing here is analysis output. r/ NICHESv2 → edges.parquet. See r/README.md. niches_xenium.R Xenium — coordinates already µm; read this one first niches_seqfish.R seqFISH — dense CSV counts, per-version coordinate units niches_visium_hd.R Visium HD — pixel coordinates, must convert to µm niches_visium.R Visium classic — same, but µm/px must be derived from the 55 µm spot spec; checks it against the 100 µm pitch + niches_merscope.R MERSCOPE — already µm; EntityID must be read as character + niches_cosmx.R CosMx — (fov, cell_ID) identity; shift to the reader's + origin, then px → µm niches_common.R shared helpers (10x h5 reader, LR-coverage check, validation) *_PPLR.R older personal pipeline with hardcoded paths; reference only docs/ @@ -697,10 +704,25 @@ Real datasets run ~0.02–0.2 and ~0.25. A passing render here is necessary, not see `sample_data/visium_hd_tiny/PROVENANCE.md`. `sample_data/visium_tiny` was built with a non-identity factor precisely to close that gap. -Bin selection defaults to `square_008um` (Space Ranger's own analysis default, and -`spatialdata-io`'s `DEFAULT_BIN`), falling back to the coarsest bin present. `info()` -reports `bin` and `available_bins`; exposing bin choice in the UI would be the natural -follow-up, in the shape of the edge-file picker. +**Bin selection follows the edge file when there is one.** Barcodes are bin-size +specific — `s_008um_00172_00043-1` and `s_016um_00066_00065-1` name different things — +so serving a different bin than `edges.parquet` was built on leaves the two with *zero* +ids in common. The edges still draw, because they carry their own coordinates, but +nothing joins: clicking a bin finds no edge, the metadata filter drops every edge, and +the tissue graph floats free of the bins beneath it. The bundled fixture shipped that way +for two releases — `niches_visium_hd.R` defaulted to 16 µm while the reader defaulted to +8 µm. + +`_bin_from_edges()` now reads one barcode from the edge file and prefers the matching +bin. Without edges it falls back to `square_008um` (Space Ranger's own analysis default, +and `spatialdata-io`'s `DEFAULT_BIN`), then to the coarsest bin present. `info()` reports +`bin` and `available_bins`; a bin picker in the UI, in the shape of the edge-file picker, +would still be the natural follow-up for datasets with no edges. + +There is a real reason the two disagreed: **8 µm bins are usually too sparse to score.** +Regenerating the fixture at 8 µm gave 233,531 edges of which 83 were scored; at 16 µm it +is 66,001 edges across 69 LRMs. Whoever runs NICHESv2 makes that call, and the viewer now +follows it. Unexploited: `segmented_outputs/cell_segmentations.geojson`, which Space Ranger 4.x emits and which would turn this from a bin viewer into a single-cell one. The seqFISH reader @@ -1168,7 +1190,7 @@ auth, no user accounts, and no per-dataset permissions. **Regression guard.** `backend/tests/golden_snapshot.py` exercises every reader method against all local datasets, digests the results, and diffs them against a recorded -baseline (217 probes across 8 datasets). Run it after any reader change: +baseline (238 probes across 8 datasets). Run it after any reader change: ```bash cd backend && python3 tests/golden_snapshot.py # check diff --git a/README.md b/README.md index 67d04dd..655acff 100644 --- a/README.md +++ b/README.md @@ -46,6 +46,8 @@ Spatial transcriptomics platforms (Xenium, seqFISH, Visium, Visium HD, MERSCOPE, | **MERSCOPE** | Vizgen | ✓ | ✓ | ✓ | ✓ | | **CosMx** | Nanostring | placeholder | ✓ | ✓ | ✓ | +All nine bundled demo datasets ship `edges.parquet` and an `edge-metadata/` folder, so the connectivity layer and the annotation workflow have something to show on every platform. Two caveats worth knowing: the MERSCOPE panel carries only **2** complete ligand-receptor pairs and the seqFISH reference panel **none**, because targeted panels are chosen for cell typing rather than signalling — `check_lr_coverage()` reports this before NICHESv2 runs, and `seqfish_instrument2` therefore uses synthetic edges from `make_edges.py`. The `edge-metadata/` folders are generated demo annotations, not analysis output; each carries a README saying which columns are derived and which is invented. + seqFISH means the commercial **Spatial Genomics GenePS** output, not the academic seqFISH/seqFISH+ method, which has no standard file layout; the current v2 layout is fully supported, and legacy v1 reads cells and transcripts but not boundaries. The two array-based platforms have no per-molecule transcript coordinates — only spot- or bin-level UMI counts — so they synthesise a polygon per unit instead: a square for a Visium HD bin, a circle for a 55 µm Visium spot. Fill, colour-by, picking and region selection then all work through the same layers. CosMx datasets often ship no morphology image, in which case TissuePlex renders the data onto a blank canvas sized to the tissue. Each reader declares what it supports via a capability flag, and the UI hides layers the platform cannot serve — and relabels itself, so a Visium dataset says "spot" wherever a Xenium one says "cell". diff --git a/backend/app/main.py b/backend/app/main.py index 0d4f784..124a35a 100644 --- a/backend/app/main.py +++ b/backend/app/main.py @@ -3,7 +3,7 @@ from app.routers import tiles, spatial, edges, layers -APP_VERSION = "0.8.2" +APP_VERSION = "0.8.3" app = FastAPI(title="TissuePlex API", version=APP_VERSION) diff --git a/backend/app/readers/visium_hd_reader.py b/backend/app/readers/visium_hd_reader.py index c728766..2569e08 100644 --- a/backend/app/readers/visium_hd_reader.py +++ b/backend/app/readers/visium_hd_reader.py @@ -41,6 +41,7 @@ emits real per-cell polygons, which would turn this from a bin viewer into a single-cell one. The seqFISH reader already has GeoJSON ring-parsing to lift. """ +import re from pathlib import Path from typing import Optional @@ -74,22 +75,72 @@ def available_bins(self) -> list[str]: return sorted(d.name for d in self._bin_root().glob("square_*um") if d.is_dir()) def _bin_dir(self) -> Optional[Path]: - """The bin directory this reader serves.""" + """The bin directory this reader serves. + + An edge file, when present, decides. Barcodes are bin-size specific — + ``s_008um_00172_00043-1`` and ``s_016um_00066_00065-1`` name different + things — so serving a different bin than ``edges.parquet`` was built on + leaves the two with zero ids in common. The edges still draw, because + they carry their own coordinates, but nothing joins: clicking a bin finds + no edge, the metadata filter drops every edge, and the tissue graph floats + free of the bins beneath it. That is exactly what the bundled fixture did + for two releases. + + Preferring the edge file's bin is also the right default on the merits. + Which bin to analyse is a real choice — 8 µm bins are often too sparse for + ligand-receptor scoring, 16 µm ones dense enough — and whoever ran + NICHESv2 already made it. Without edges, fall back to Space Ranger's own + analysis default. + """ if self._bin_dir_cache is not _UNSET: return self._bin_dir_cache # type: ignore[return-value] root = self._bin_root() available = self.available_bins() chosen = None - for name in _PREFERRED_BINS: - if name in available: - chosen = root / name - break + + from_edges = self._bin_from_edges() + if from_edges and from_edges in available: + chosen = root / from_edges + if chosen is None: + for name in _PREFERRED_BINS: + if name in available: + chosen = root / name + break if chosen is None and available: # Unknown bin size — take the coarsest, i.e. the largest µm number. chosen = root / sorted(available)[-1] self._bin_dir_cache = chosen return chosen + def _bin_from_edges(self) -> Optional[str]: + """Bin directory name implied by the barcodes in an edge file, or None. + + Reads a single row. Visium HD barcodes are ``s_um__-1``, + so one is enough to name the bin. + """ + candidates = [] + top = self.path / "edges.parquet" + if top.exists(): + candidates.append(top) + edges_dir = self.path / "edges" + if edges_dir.is_dir(): + candidates.extend(sorted(edges_dir.glob("*.parquet"))) + + for candidate in candidates: + try: + import pyarrow.parquet as pq + if "sending_cell" not in set(pq.read_schema(candidate).names): + continue + batch = next(pq.ParquetFile(candidate) + .iter_batches(batch_size=1, columns=["sending_cell"])) + bc = str(batch.column(0)[0]) + except Exception: + continue + m = re.match(r"s_(\d+)um_", bc) + if m: + return f"square_{m.group(1)}um" + return None + # ── Space Ranger hooks ──────────────────────────────────────────────────── def _spatial_dir(self) -> Optional[Path]: diff --git a/backend/tests/golden_baseline.json b/backend/tests/golden_baseline.json index 156299b..4d7c5a7 100644 --- a/backend/tests/golden_baseline.json +++ b/backend/tests/golden_baseline.json @@ -102,6 +102,83 @@ "n": 38996, "type": "continuous" }, + "edge__edges.parquet__catalogue": { + "digest": "500ee67d465bddb9", + "n": 228 + }, + "edge__edges.parquet__color_lrm": { + "digest": "2a4ca0e892dfba39" + }, + "edge__edges.parquet__detail0": "22d972d6c4cd5d68", + "edge__edges.parquet__filter__demo_review_flag": { + "digest": "103daa3605cfad32", + "keys": [ + "edge", + "is_autocrine", + "lrm_count", + "receiving_cell", + "receiving_type", + "score_sum", + "sending_cell", + "sending_type", + "x1", + "x2", + "y1", + "y2" + ], + "n": 56352 + }, + "edge__edges.parquet__grouped": { + "digest": "a9dd1ab6cea29d56", + "keys": [ + "edge", + "is_autocrine", + "lrm_count", + "receiving_cell", + "receiving_type", + "score_sum", + "sending_cell", + "sending_type", + "x1", + "x2", + "y1", + "y2" + ], + "n": 169219 + }, + "edge__edges.parquet__schema": { + "columns": { + "demo_review_flag": "object", + "edge": "string", + "interaction_class": "object", + "is_autocrine": "bool", + "ligand": "string", + "lrm": "string", + "lrm_id": "int32", + "receiving_cell": "string", + "receiving_type": "string", + "receptor": "string", + "score": "double", + "score_norm": "double", + "score_percentile": "float64", + "score_tier": "object", + "sending_cell": "string", + "sending_type": "string", + "x1": "double", + "x2": "double", + "y1": "double", + "y2": "double" + } + }, + "edge__edges.parquet__scores": { + "digest": "dd79c456b7ea4afd", + "keys": [ + "edge", + "visible_lrm_count", + "visible_score_sum" + ], + "n": 169219 + }, "gene_list": { "digest": "b4a6b682f016cebd", "n": 960 @@ -237,6 +314,83 @@ "n": 997, "type": "continuous" }, + "edge__edges.parquet__catalogue": { + "digest": "357d0d285ccf1913", + "n": 2 + }, + "edge__edges.parquet__color_lrm": { + "digest": "08feecc7b4935684" + }, + "edge__edges.parquet__detail0": "4c48f3a980c1e7e6", + "edge__edges.parquet__filter__demo_review_flag": { + "digest": "aa3efaa2908ea3f2", + "keys": [ + "edge", + "is_autocrine", + "lrm_count", + "receiving_cell", + "receiving_type", + "score_sum", + "sending_cell", + "sending_type", + "x1", + "x2", + "y1", + "y2" + ], + "n": 4835 + }, + "edge__edges.parquet__grouped": { + "digest": "e17b16984347c091", + "keys": [ + "edge", + "is_autocrine", + "lrm_count", + "receiving_cell", + "receiving_type", + "score_sum", + "sending_cell", + "sending_type", + "x1", + "x2", + "y1", + "y2" + ], + "n": 14503 + }, + "edge__edges.parquet__schema": { + "columns": { + "demo_review_flag": "object", + "edge": "string", + "interaction_class": "object", + "is_autocrine": "bool", + "ligand": "string", + "lrm": "string", + "lrm_id": "int32", + "receiving_cell": "string", + "receiving_type": "string", + "receptor": "string", + "score": "double", + "score_norm": "double", + "score_percentile": "float64", + "score_tier": "object", + "sending_cell": "string", + "sending_type": "string", + "x1": "double", + "x2": "double", + "y1": "double", + "y2": "double" + } + }, + "edge__edges.parquet__scores": { + "digest": "f5281da9d1bfd9f0", + "keys": [ + "edge", + "visible_lrm_count", + "visible_score_sum" + ], + "n": 14503 + }, "gene_list": { "digest": "ba0a34a6d5631ace", "n": 130 @@ -402,9 +556,9 @@ "edge__edges.parquet__color_lrm": { "digest": "c92dc2b2c78706bf" }, - "edge__edges.parquet__detail0": "b5b16c6db1ee824b", - "edge__edges.parquet__filter__interaction_class": { - "digest": "868bc3eaf596bf9c", + "edge__edges.parquet__detail0": "74839d3b778ee5f3", + "edge__edges.parquet__filter__demo_review_flag": { + "digest": "72d9521da6c1665a", "keys": [ "edge", "is_autocrine", @@ -419,7 +573,7 @@ "y1", "y2" ], - "n": 176 + "n": 73 }, "edge__edges.parquet__grouped": { "digest": "74439cac91762e89", @@ -441,7 +595,7 @@ }, "edge__edges.parquet__schema": { "columns": { - "confidence": "float64", + "demo_review_flag": "object", "edge": "string", "interaction_class": "object", "is_autocrine": "bool", @@ -451,9 +605,10 @@ "receiving_cell": "string", "receiving_type": "string", "receptor": "string", - "reviewed": "object", "score": "double", "score_norm": "double", + "score_percentile": "float64", + "score_tier": "object", "sending_cell": "string", "sending_type": "string", "x1": "double", @@ -478,9 +633,9 @@ "edge__edges__edge.normalized.product.parquet__color_lrm": { "digest": "519a5ef75304e432" }, - "edge__edges__edge.normalized.product.parquet__detail0": "e5a6e867ae7774fa", - "edge__edges__edge.normalized.product.parquet__filter__interaction_class": { - "digest": "7106481dad64d458", + "edge__edges__edge.normalized.product.parquet__detail0": "868d094eba0c7ec3", + "edge__edges__edge.normalized.product.parquet__filter__demo_review_flag": { + "digest": "145ff6e7b7a1229d", "keys": [ "edge", "is_autocrine", @@ -495,7 +650,7 @@ "y1", "y2" ], - "n": 176 + "n": 87 }, "edge__edges__edge.normalized.product.parquet__grouped": { "digest": "60404aba71d2d61d", @@ -517,7 +672,7 @@ }, "edge__edges__edge.normalized.product.parquet__schema": { "columns": { - "confidence": "float64", + "demo_review_flag": "object", "edge": "string", "interaction_class": "object", "is_autocrine": "bool", @@ -527,9 +682,10 @@ "receiving_cell": "string", "receiving_type": "string", "receptor": "string", - "reviewed": "object", "score": "double", "score_norm": "double", + "score_percentile": "float64", + "score_tier": "object", "sending_cell": "string", "sending_type": "string", "x1": "double", @@ -554,9 +710,9 @@ "edge__edges__edge.raw.minimum.parquet__color_lrm": { "digest": "ea178fdb2da7ef01" }, - "edge__edges__edge.raw.minimum.parquet__detail0": "1653c7e83d4142bd", - "edge__edges__edge.raw.minimum.parquet__filter__interaction_class": { - "digest": "46e42445db77b41e", + "edge__edges__edge.raw.minimum.parquet__detail0": "b7354292f85f774c", + "edge__edges__edge.raw.minimum.parquet__filter__demo_review_flag": { + "digest": "7257bc5681e50648", "keys": [ "edge", "is_autocrine", @@ -571,7 +727,7 @@ "y1", "y2" ], - "n": 113 + "n": 51 }, "edge__edges__edge.raw.minimum.parquet__grouped": { "digest": "ba6b68c6da29e63c", @@ -593,7 +749,7 @@ }, "edge__edges__edge.raw.minimum.parquet__schema": { "columns": { - "confidence": "float64", + "demo_review_flag": "object", "edge": "string", "interaction_class": "object", "is_autocrine": "bool", @@ -603,9 +759,10 @@ "receiving_cell": "string", "receiving_type": "string", "receptor": "string", - "reviewed": "object", "score": "double", "score_norm": "double", + "score_percentile": "float64", + "score_tier": "object", "sending_cell": "string", "sending_type": "string", "x1": "double", @@ -796,6 +953,83 @@ "n": 62, "type": "continuous" }, + "edge__edges.parquet__catalogue": { + "digest": "556c70f23429ddd8", + "n": 20 + }, + "edge__edges.parquet__color_lrm": { + "digest": "bb18f26526ace634" + }, + "edge__edges.parquet__detail0": "578faa4d300aa511", + "edge__edges.parquet__filter__demo_review_flag": { + "digest": "5de556005faf2769", + "keys": [ + "edge", + "is_autocrine", + "lrm_count", + "receiving_cell", + "receiving_type", + "score_sum", + "sending_cell", + "sending_type", + "x1", + "x2", + "y1", + "y2" + ], + "n": 120 + }, + "edge__edges.parquet__grouped": { + "digest": "445660bca7bbe1bf", + "keys": [ + "edge", + "is_autocrine", + "lrm_count", + "receiving_cell", + "receiving_type", + "score_sum", + "sending_cell", + "sending_type", + "x1", + "x2", + "y1", + "y2" + ], + "n": 372 + }, + "edge__edges.parquet__schema": { + "columns": { + "demo_review_flag": "object", + "edge": "string", + "interaction_class": "object", + "is_autocrine": "bool", + "ligand": "string", + "lrm": "string", + "lrm_id": "int64", + "receiving_cell": "string", + "receiving_type": "string", + "receptor": "string", + "score": "double", + "score_norm": "double", + "score_percentile": "float64", + "score_tier": "object", + "sending_cell": "string", + "sending_type": "string", + "x1": "double", + "x2": "double", + "y1": "double", + "y2": "double" + } + }, + "edge__edges.parquet__scores": { + "digest": "5ab69ff9baedcf5a", + "keys": [ + "edge", + "visible_lrm_count", + "visible_score_sum" + ], + "n": 372 + }, "gene_list": { "digest": "6f1e76b0bd839528", "n": 12 @@ -933,9 +1167,9 @@ "edge__edges.parquet__color_lrm": { "digest": "147980b074db33d3" }, - "edge__edges.parquet__detail0": "78d7e0433da4808e", - "edge__edges.parquet__filter__is_autocrine": { - "digest": "99f18f8571d0f208", + "edge__edges.parquet__detail0": "86c149dfe39d48df", + "edge__edges.parquet__filter__demo_review_flag": { + "digest": "b5a517781cb61ae3", "keys": [ "edge", "is_autocrine", @@ -950,7 +1184,7 @@ "y1", "y2" ], - "n": 214 + "n": 83 }, "edge__edges.parquet__grouped": { "digest": "95d8d03ded9a9ecb", @@ -972,7 +1206,9 @@ }, "edge__edges.parquet__schema": { "columns": { + "demo_review_flag": "object", "edge": "string", + "interaction_class": "object", "is_autocrine": "bool", "ligand": "string", "lrm": "string", @@ -982,6 +1218,8 @@ "receptor": "string", "score": "double", "score_norm": "double", + "score_percentile": "float64", + "score_tier": "object", "sending_cell": "string", "sending_type": "string", "x1": "double", @@ -1056,21 +1294,21 @@ }, "visium_hd_tiny": { "bounds_full": { - "digest": "cd35b92208180b99", + "digest": "65bffb9770ac8d37", "keys": [ "cell_id", "vertex_x", "vertex_y" ], - "n": 83320, - "n_cells": 20830, - "total": 20830 + "n": 21048, + "n_cells": 5262, + "total": 5262 }, "bounds_half": { - "digest": "95081e497eddedcf", - "n": 41660, - "n_cells": 10415, - "total": 20830 + "digest": "f29cceca56ac530f", + "n": 10524, + "n_cells": 2631, + "total": 5262 }, "capabilities": { "has_boundaries": true, @@ -1078,13 +1316,13 @@ "has_transcripts": false, "unit_label": "bin" }, - "cell_detail_0": "335cd56d146cb6f7", + "cell_detail_0": "38a2a938bc169f6c", "cell_expression_0": { "digest": "44136fa355b3678a", "n": 0 }, "cells_all": { - "digest": "86c7b0d0860673e8", + "digest": "78d7c770d606e3be", "keys": [ "array_col", "array_row", @@ -1092,7 +1330,7 @@ "x_centroid", "y_centroid" ], - "n": 20830 + "n": 5262 }, "cells_schema": { "columns": { @@ -1103,32 +1341,32 @@ } }, "color_forced_cat__array_row": { - "categories_digest": "cb1f070cb9fb1832", - "digest": "e2371587c0c48cbc", - "n": 20830, - "n_categories": 98, + "categories_digest": "2b94e8ec47e35448", + "digest": "37c3904b2c7020bf", + "n": 5262, + "n_categories": 50, "type": "categorical" }, "color_gene_set": { - "digest": "654b57fb083efe14", + "digest": "cca6ee7602b858db", "max": 1.0, "min": 0.0, - "n": 20830, + "n": 5262, "type": "continuous" }, "color_meta__array_row": { - "digest": "e437cf85ebbc2bf8", - "n": 20830, + "digest": "e2aea55ab9c5939c", + "n": 5262, "type": "continuous" }, "color_meta__x_centroid": { - "digest": "c41021fad7667686", - "n": 20830, + "digest": "2d0c27d66cff1717", + "n": 5262, "type": "continuous" }, "color_meta__y_centroid": { - "digest": "026a12c0bd52c1d9", - "n": 20830, + "digest": "1b35a40fb69e1249", + "n": 5262, "type": "continuous" }, "edge__edges.parquet__catalogue": { @@ -1138,9 +1376,9 @@ "edge__edges.parquet__color_lrm": { "digest": "d64c43b4d5cff51c" }, - "edge__edges.parquet__detail0": "89d54bdc782c25cb", - "edge__edges.parquet__filter__is_autocrine": { - "digest": "794574a264114c51", + "edge__edges.parquet__detail0": "ab904342900f1285", + "edge__edges.parquet__filter__demo_review_flag": { + "digest": "fd847d78d45a254e", "keys": [ "edge", "is_autocrine", @@ -1155,7 +1393,7 @@ "y1", "y2" ], - "n": 62680 + "n": 22082 }, "edge__edges.parquet__grouped": { "digest": "8a6fa8e717282f31", @@ -1177,7 +1415,9 @@ }, "edge__edges.parquet__schema": { "columns": { + "demo_review_flag": "object", "edge": "string", + "interaction_class": "object", "is_autocrine": "bool", "ligand": "string", "lrm": "string", @@ -1187,6 +1427,8 @@ "receptor": "string", "score": "double", "score_norm": "double", + "score_percentile": "float64", + "score_tier": "object", "sending_cell": "string", "sending_type": "string", "x1": "double", @@ -1311,9 +1553,9 @@ "edge__edges.parquet__color_lrm": { "digest": "191fa6a66f988117" }, - "edge__edges.parquet__detail0": "ad6b47ab3dad15bd", - "edge__edges.parquet__filter__is_autocrine": { - "digest": "7db54e9548c42b48", + "edge__edges.parquet__detail0": "8a0ea0618fc27de1", + "edge__edges.parquet__filter__demo_review_flag": { + "digest": "c97d4b5e0940d6ae", "keys": [ "edge", "is_autocrine", @@ -1328,7 +1570,7 @@ "y1", "y2" ], - "n": 1390 + "n": 579 }, "edge__edges.parquet__grouped": { "digest": "43b2a883106031e3", @@ -1350,7 +1592,9 @@ }, "edge__edges.parquet__schema": { "columns": { + "demo_review_flag": "object", "edge": "string", + "interaction_class": "object", "is_autocrine": "bool", "ligand": "string", "lrm": "string", @@ -1360,6 +1604,8 @@ "receptor": "string", "score": "double", "score_norm": "double", + "score_percentile": "float64", + "score_tier": "object", "sending_cell": "string", "sending_type": "string", "x1": "double", @@ -1507,9 +1753,9 @@ "edge__edges.parquet__color_lrm": { "digest": "f454e9ec05ba3d82" }, - "edge__edges.parquet__detail0": "502de7ce02b60495", - "edge__edges.parquet__filter__is_autocrine": { - "digest": "cb64d837bdcc7877", + "edge__edges.parquet__detail0": "07e4a0feab68ed99", + "edge__edges.parquet__filter__demo_review_flag": { + "digest": "2104bf58593a5e26", "keys": [ "edge", "is_autocrine", @@ -1524,7 +1770,7 @@ "y1", "y2" ], - "n": 43650 + "n": 14781 }, "edge__edges.parquet__grouped": { "digest": "2587c77fd6f6a1b3", @@ -1546,7 +1792,9 @@ }, "edge__edges.parquet__schema": { "columns": { + "demo_review_flag": "object", "edge": "string", + "interaction_class": "object", "is_autocrine": "bool", "ligand": "string", "lrm": "string", @@ -1556,6 +1804,8 @@ "receptor": "string", "score": "double", "score_norm": "double", + "score_percentile": "float64", + "score_tier": "object", "sending_cell": "string", "sending_type": "string", "x1": "double", @@ -1580,9 +1830,9 @@ "edge__edges__niches_rad30.parquet__color_lrm": { "digest": "f746905c1d52f0aa" }, - "edge__edges__niches_rad30.parquet__detail0": "0635850ff026fa23", - "edge__edges__niches_rad30.parquet__filter__is_autocrine": { - "digest": "1dea970e0700b956", + "edge__edges__niches_rad30.parquet__detail0": "b3e4808506fe2c80", + "edge__edges__niches_rad30.parquet__filter__demo_review_flag": { + "digest": "bd6aef6053d1632d", "keys": [ "edge", "is_autocrine", @@ -1597,7 +1847,7 @@ "y1", "y2" ], - "n": 174248 + "n": 60623 }, "edge__edges__niches_rad30.parquet__grouped": { "digest": "d2e6d82664d037f3", @@ -1619,7 +1869,9 @@ }, "edge__edges__niches_rad30.parquet__schema": { "columns": { + "demo_review_flag": "object", "edge": "string", + "interaction_class": "object", "is_autocrine": "bool", "ligand": "string", "lrm": "string", @@ -1629,6 +1881,8 @@ "receptor": "string", "score": "double", "score_norm": "double", + "score_percentile": "float64", + "score_tier": "object", "sending_cell": "string", "sending_type": "string", "x1": "double", diff --git a/frontend/package.json b/frontend/package.json index 999c015..76f4836 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -1,6 +1,6 @@ { "name": "tissueplex", - "version": "0.8.2", + "version": "0.8.3", "private": true, "scripts": { "dev": "vite", diff --git a/frontend/src/components/LayerPanel.jsx b/frontend/src/components/LayerPanel.jsx index 89eeff8..f7a469a 100644 --- a/frontend/src/components/LayerPanel.jsx +++ b/frontend/src/components/LayerPanel.jsx @@ -244,6 +244,9 @@ function ColorBySection({ unitLabel = "cell" }) { // Fetch full gene list and cell schema once per dataset useEffect(() => { + // `dataset` is null until DatasetPicker resolves the list; fetching then + // just 404s against /spatial/null/... and buries real errors in the console. + if (!dataset) return; setGenesLoaded(false); fetch(`${apiBase}/spatial/${dataset}/genes`) .then((r) => r.ok ? r.json() : []) @@ -729,6 +732,7 @@ function CellFilterSection({ unitLabel = "cell" }) { const [columns, setColumns] = useState([]); useEffect(() => { + if (!dataset) return; fetch(`${apiBase}/spatial/${dataset}/cells/schema`) .then((r) => (r.ok ? r.json() : null)) .then((s) => setColumns(s?.columns ? Object.keys(s.columns) : [])) @@ -760,6 +764,7 @@ function EdgeFilterSection() { const efParam = `?edge_file=${encodeURIComponent(edgeFile)}`; useEffect(() => { + if (!dataset) return; fetch(`${apiBase}/edges/${dataset}/schema${efParam}`) .then((r) => (r.ok ? r.json() : null)) // Structural and per-LRM columns are not edge attributes to subset on: @@ -1307,6 +1312,7 @@ function EdgeSection() { const efParam = `?edge_file=${encodeURIComponent(edgeFile)}`; useEffect(() => { + if (!dataset) return; fetch(`${apiBase}/edges/${dataset}/schema${efParam}`) .then((r) => (r.ok ? r.json() : null)) .then(setEdgeSchema) diff --git a/r/README.md b/r/README.md index c7e01ac..96e9688 100644 --- a/r/README.md +++ b/r/README.md @@ -9,11 +9,21 @@ but the first two steps differ enough per platform to be worth reading separatel | `niches_xenium.R` | Xenium | The simple case. Coordinates are already µm; nothing to convert. **Read this first.** | | `niches_seqfish.R` | seqFISH (Spatial Genomics GenePS) | Counts are a dense cells × genes CSV needing transposition; coordinate units vary by GenePS version, so they are detected and reported. | | `niches_visium_hd.R` | Visium HD | Coordinates are **pixels** and must be multiplied by `microns_per_pixel` before NICHESv2 sees them. | +| `niches_merscope.R` | MERSCOPE (Vizgen) | The easy case: `cell_metadata.csv` is already in microns. But EntityID is a 19-digit integer that R silently mangles as numeric, and small panels often carry no complete LR pair. | +| `niches_cosmx.R` | CosMx (Nanostring/Bruker) | Cell identity is the `(fov, cell_ID)` pair, and coordinates are slide-frame **pixels** that must be shifted to the reader's origin *then* scaled to microns. | | `niches_visium.R` | Visium (classic) | Same pixel problem, but there is no `microns_per_pixel` to multiply by — it is derived from the 55 µm spot spec, then sanity-checked against the known 100 µm lattice pitch. | | `niches_common.R` | — | Shared helpers only: the 10x HDF5 reader, barcode alignment, LR-coverage check, output validation. Not runnable. | The older `*_PPLR.R` scripts are a personal analysis pipeline with hardcoded paths, kept -for reference. The five above are the ones to copy. +for reference. The seven above are the ones to copy — one per supported platform. + +**Barcodes must match what the viewer serves.** `export_to_TissuePlex()` writes cell ids +into `sending_cell` / `receiving_cell`, and TissuePlex joins the edge layer to its units on +those strings. Get the id scheme wrong and the edges still draw — they carry their own +coordinates — but nothing joins: clicking a unit finds no edge and the metadata filter +drops everything. Visium HD is the live example: barcodes are bin-size specific, so an +edge file built at 16 µm shares zero ids with the 8 µm bins. The reader now picks its bin +from the edge file to keep the two in step. ## Setup @@ -33,6 +43,8 @@ Rscript r/niches_xenium.R sample_data/xenium_human_breast_2fov --species huma Rscript r/niches_seqfish.R sample_data/seqfish_synthetic --species mouse --rad 12 Rscript r/niches_visium_hd.R sample_data/visium_hd_tiny --bin square_016um --rad 40 Rscript r/niches_visium.R sample_data/visium_tiny --species mouse --rad 150 +Rscript r/niches_merscope.R sample_data/merscope-vpt-smallset --species human --rad 30 +Rscript r/niches_cosmx.R sample_data/cosmx-mousebrain --species mouse --rad 20 ``` Each writes `edges.parquet` into the dataset folder, where TissuePlex picks it up with no diff --git a/r/niches_cosmx.R b/r/niches_cosmx.R new file mode 100644 index 0000000..bf1aefe --- /dev/null +++ b/r/niches_cosmx.R @@ -0,0 +1,191 @@ +#!/usr/bin/env Rscript +## niches_cosmx.R -------------------------------------------------------------- +## +## Run NICHESv2 on a Nanostring/Bruker CosMx dataset and write edges.parquet for +## TissuePlex. +## +## Rscript r/niches_cosmx.R [dataset_dir] [--rad 30] [--species mouse] +## Rscript r/niches_cosmx.R sample_data/cosmx-mousebrain +## +## Read niches_xenium.R first for the shape of the pipeline. CosMx adds two +## wrinkles that will silently misplace every edge if you skip them. +## +## 1. Cell identity is the (fov, cell_ID) PAIR +## ------------------------------------------- +## `cell_ID` restarts at 1 in every field of view, so keying on it alone merges +## unrelated cells across the slide. The TissuePlex reader builds its cell_id as +## `_` and the barcodes in edges.parquet must match exactly, or the +## edge layer will not join to a single cell. Note the column is spelled `cell_ID` +## in the metadata and expression files and `cellID` in the polygons file. +## +## 2. Coordinates are PIXELS in the slide frame, and the reader shifts them +## ----------------------------------------------------------------------- +## CosMx exports carry no morphology image, so there is no external frame to +## define image-pixel space. The reader therefore shifts the whole dataset to its +## own corner: +## +## origin = (min(CenterX_global_px) - 50, min(CenterY_global_px) - 50) +## cell image position = CenterX_global_px - origin_x +## +## and TissuePlex divides the microns in edges.parquet by `pixel_size` to recover +## that. So the conversion here has to be shift THEN scale: +## +## x_um = (CenterX_global_px - origin_x) * microns_per_pixel +## +## Skip the shift and the edge layer is offset by tens of thousands of pixels; +## skip the scale and it is off by a factor of ~8. The -50 margin is not cosmetic +## — it is part of the reader's origin and must be reproduced. +## +## `microns_per_pixel` is 0.12028 for CosMx (Bruker's documented value, matching +## the reader default). Override with --mpp if your instrument differs. +## +## Inputs (flat CosMx export, files usually prefixed with the experiment name) +## *_exprMat_file.csv fov, cell_ID, then one column per gene +## *_metadata_file.csv fov, cell_ID, CenterX_global_px, CenterY_global_px, ... +## +## Requires: NICHESv2 (dev branch), arrow, Matrix, data.table. + +suppressPackageStartupMessages({ + library(Matrix); library(arrow); library(data.table); library(NICHESv2) +}) + +.this <- tryCatch({ + a <- commandArgs(trailingOnly = FALSE) + normalizePath(sub("^--file=", "", a[grep("^--file=", a)][1])) +}, error = function(e) NA_character_) +source(file.path(if (is.na(.this)) "r" else dirname(.this), "niches_common.R")) + +# Bruker's documented CosMx pixel size, and the TissuePlex reader default. +DEFAULT_MPP <- 0.12028 +# Must match cosmx_reader.py::_origin(), which leaves this margin so units on the +# edge are not flush against the placeholder canvas border. +ORIGIN_MARGIN_PX <- 50 + +# ── Parameters ──────────────────────────────────────────────────────────────── +args <- commandArgs(trailingOnly = TRUE) +opt <- function(flag, default) { + i <- match(flag, args); if (is.na(i) || i == length(args)) default else args[i + 1L] +} +positional <- args[!grepl("^--", args) & + !(seq_along(args) %in% (which(grepl("^--", args)) + 1L))] + +DATASET <- if (length(positional)) positional[1] else "sample_data/cosmx-mousebrain" +OUT <- opt("--out", file.path(DATASET, "edges.parquet")) +SPECIES <- opt("--species", "mouse") +# rad in MICRONS. CosMx cells run ~10 um, so 30 um reaches the first ring of +# neighbours. Raising it on a 40k-cell slide multiplies the row count fast. +RAD <- as.numeric(opt("--rad", "30")) +CORES <- as.integer(opt("--cores", "4")) +MINCOUNT <- as.integer(opt("--min-counts", "1")) +MPP <- as.numeric(opt("--mpp", DEFAULT_MPP)) +# Restrict to a subset of FOVs. A whole CosMx slide can produce tens of millions +# of edge x LRM rows; this keeps a demo or a pilot to a workable size. +FOVS <- opt("--fovs", NA_character_) + +cat("── NICHESv2 · CosMx (Nanostring/Bruker) ──────────────────────\n") +cat(" dataset :", DATASET, "\n output :", OUT, "\n") +cat(" species :", SPECIES, " rad:", RAD, "um cores:", CORES, "\n") +cat(" um/px :", MPP, "\n\n") + +find1 <- function(patterns) { + for (p in patterns) { + hit <- Sys.glob(file.path(DATASET, p)) + if (length(hit)) return(hit[1]) + } + NA_character_ +} + +# ── 1. Counts: cells x genes -> genes x cells ──────────────────────────────── +cat("reading counts…\n") +expr.path <- find1(c("*_exprMat_file.csv", "exprMat_file.csv")) +if (is.na(expr.path)) stop("no *_exprMat_file.csv in ", DATASET) +expr <- data.table::fread(expr.path) +for (need in c("fov", "cell_ID")) + if (!need %in% names(expr)) stop("missing column '", need, "' in ", basename(expr.path)) + +# THE key. Must match cosmx_reader.py, which builds "_". +expr.ids <- paste0(expr$fov, "_", expr$cell_ID) +gene.cols <- setdiff(names(expr), c("fov", "cell_ID")) +count.mtx <- Matrix::Matrix(t(as.matrix(expr[, ..gene.cols])), sparse = TRUE) +colnames(count.mtx) <- expr.ids +rm(expr); invisible(gc()) +cat(sprintf(" %d genes x %d cells\n", nrow(count.mtx), ncol(count.mtx))) + +# CosMx panels carry Negative/SystemControl probes; they are not genes. +ctrl <- grepl("^(Negative|NegPrb|SystemControl|Custom)", rownames(count.mtx)) +if (any(ctrl)) { + cat(sprintf(" dropping %d control probes\n", sum(ctrl))) + count.mtx <- count.mtx[!ctrl, , drop = FALSE] +} + +# ── 2. Positions: shift to the reader's origin, then pixels -> microns ─────── +cat("reading cell metadata…\n") +meta.path <- find1(c("*_metadata_file.csv", "metadata_file.csv")) +if (is.na(meta.path)) stop("no *_metadata_file.csv in ", DATASET) +md <- data.table::fread(meta.path) +for (need in c("fov", "cell_ID", "CenterX_global_px", "CenterY_global_px")) + if (!need %in% names(md)) stop("missing column '", need, "' in ", basename(meta.path)) + +if (!is.na(FOVS)) { + keep.fov <- as.integer(strsplit(FOVS, ",")[[1]]) + md <- md[md$fov %in% keep.fov, ] + cat(sprintf(" restricted to FOV %s -> %d cells\n", FOVS, nrow(md))) +} + +# The origin is computed over the WHOLE metadata file, not the FOV subset, so a +# subset stays registered with what the reader draws. +md.all <- data.table::fread(meta.path, select = c("CenterX_global_px", "CenterY_global_px")) +x0 <- min(md.all$CenterX_global_px, na.rm = TRUE) - ORIGIN_MARGIN_PX +y0 <- min(md.all$CenterY_global_px, na.rm = TRUE) - ORIGIN_MARGIN_PX +rm(md.all); invisible(gc()) +cat(sprintf(" origin (min - %d px): x0=%.1f y0=%.1f\n", ORIGIN_MARGIN_PX, x0, y0)) + +meta.data <- data.frame( + x = (as.numeric(md$CenterX_global_px) - x0) * MPP, # shift THEN scale + y = (as.numeric(md$CenterY_global_px) - y0) * MPP, + row.names = paste0(md$fov, "_", md$cell_ID), + stringsAsFactors = FALSE +) +cat(sprintf(" shifted to the reader origin, then px -> um (x %.5f)\n", MPP)) +report_extent(meta.data, "um") + +aligned <- align_counts_meta(count.mtx, meta.data) +count.mtx <- aligned$count.mtx +meta.data <- aligned$meta.data + +if (MINCOUNT > 0) { + keep <- Matrix::colSums(count.mtx) >= MINCOUNT + cat(sprintf(" dropping %d cells with < %d counts (%d remain)\n", + sum(!keep), MINCOUNT, sum(keep))) + count.mtx <- count.mtx[, keep, drop = FALSE] + meta.data <- meta.data[keep, , drop = FALSE] +} + +# ── 3. Build and export ─────────────────────────────────────────────────────── +check_lr_coverage(count.mtx, SPECIES) + +cat(sprintf("\nrunning NICHESv2 (spatial mode) on %d cells…\n", ncol(count.mtx))) +if (ncol(count.mtx) > 20000) + cat(" note: a slide this size can produce tens of millions of edge x LRM rows.\n", + " Use --fovs or a smaller --rad if the output is unwieldy.\n", sep = "") + +obj <- create_NICHESObject( + count.mtx = count.mtx, + meta.data = meta.data, + LRM.db = "connectomedb2025", + species = SPECIES, + mode = "spatial", + rad = RAD, # microns, matching meta.data$x/$y + method = "product", + normalize.method = "prop", + n.cores = CORES, + verbose = TRUE +) + +cat("\nexporting…\n") +export_to_TissuePlex(obj, output.path = OUT, + x.col = "x", y.col = "y", + celltype.col = NULL, n.threads = CORES) + +validate_edges_parquet(OUT) +cat("\ndone — reload TissuePlex to see the edge layer.\n") diff --git a/r/niches_merscope.R b/r/niches_merscope.R new file mode 100644 index 0000000..ca1b83f --- /dev/null +++ b/r/niches_merscope.R @@ -0,0 +1,142 @@ +#!/usr/bin/env Rscript +## niches_merscope.R ----------------------------------------------------------- +## +## Run NICHESv2 on a Vizgen MERSCOPE dataset and write edges.parquet for +## TissuePlex. +## +## Rscript r/niches_merscope.R [dataset_dir] [--rad 30] [--species human] +## Rscript r/niches_merscope.R sample_data/merscope-vpt-smallset +## +## MERSCOPE is the easy case for coordinates — `cell_metadata.csv` stores +## center_x / center_y in MICRONS already, which is what export_to_TissuePlex() +## wants, so nothing is converted. Read niches_xenium.R first; this is the same +## shape with a different pair of input files. +## +## The two things that do bite +## --------------------------- +## 1. **EntityID is a 19-digit integer.** R silently loses precision on those if +## they are read as numeric, which corrupts every barcode and makes the join +## against the count matrix fail — or worse, succeed partially. Both files are +## read with the id column forced to character. +## +## 2. **Targeted panels often cannot be scored.** MERSCOPE panels run 100-500 +## genes chosen for cell typing, not for signalling, so a complete +## ligand-receptor pair (both partners on the panel) is the exception. The +## bundled VPT test set has 130 genes and exactly 2 scorable pairs. That is a +## property of the panel, not a failure — check_lr_coverage() reports it before +## NICHESv2 runs so you find out in one second rather than ten minutes. +## +## Inputs +## cell_by_gene.csv first column = EntityID, remaining columns = genes +## cell_metadata.csv EntityID, fov, volume, center_x, center_y, ... +## +## Requires: NICHESv2 (dev branch), arrow, Matrix, data.table. + +suppressPackageStartupMessages({ + library(Matrix); library(arrow); library(data.table); library(NICHESv2) +}) + +.this <- tryCatch({ + a <- commandArgs(trailingOnly = FALSE) + normalizePath(sub("^--file=", "", a[grep("^--file=", a)][1])) +}, error = function(e) NA_character_) +source(file.path(if (is.na(.this)) "r" else dirname(.this), "niches_common.R")) + +# ── Parameters ──────────────────────────────────────────────────────────────── +args <- commandArgs(trailingOnly = TRUE) +opt <- function(flag, default) { + i <- match(flag, args); if (is.na(i) || i == length(args)) default else args[i + 1L] +} +positional <- args[!grepl("^--", args) & + !(seq_along(args) %in% (which(grepl("^--", args)) + 1L))] + +DATASET <- if (length(positional)) positional[1] else "sample_data/merscope-vpt-smallset" +OUT <- opt("--out", file.path(DATASET, "edges.parquet")) +# The bundled VPT test set is human. Vizgen ships mouse panels too — the gene +# symbols tell you which: uppercase is human, Title case is mouse. +SPECIES <- opt("--species", "human") +# rad in MICRONS, matching center_x/center_y. MERSCOPE cells run ~10 um, so 30 um +# reaches the immediate neighbours. +RAD <- as.numeric(opt("--rad", "30")) +CORES <- as.integer(opt("--cores", "4")) +MINCOUNT <- as.integer(opt("--min-counts", "1")) + +cat("── NICHESv2 · MERSCOPE (Vizgen) ──────────────────────────────\n") +cat(" dataset :", DATASET, "\n output :", OUT, "\n") +cat(" species :", SPECIES, " rad:", RAD, "um cores:", CORES, "\n\n") + +# ── 1. Counts: cells x genes -> genes x cells ──────────────────────────────── +cat("reading counts…\n") +cbg.path <- file.path(DATASET, "cell_by_gene.csv") +if (!file.exists(cbg.path)) stop("no cell_by_gene.csv in ", DATASET) +# colClasses on the first column only: everything else is numeric counts. +cbg <- data.table::fread(cbg.path, colClasses = list(character = 1)) +id.col <- names(cbg)[1] +cells.id <- as.character(cbg[[id.col]]) +count.mtx <- Matrix::Matrix(t(as.matrix(cbg[, -1, with = FALSE])), sparse = TRUE) +colnames(count.mtx) <- cells.id +cat(sprintf(" %d genes x %d cells\n", nrow(count.mtx), ncol(count.mtx))) + +# Vizgen panels carry Blank-* control probes; they are not genes. +ctrl <- grepl("^(Blank|blank)", rownames(count.mtx)) +if (any(ctrl)) { + cat(sprintf(" dropping %d blank control probes\n", sum(ctrl))) + count.mtx <- count.mtx[!ctrl, , drop = FALSE] +} + +# ── 2. Metadata: already microns, nothing to convert ───────────────────────── +cat("reading cell metadata…\n") +meta.path <- file.path(DATASET, "cell_metadata.csv") +if (!file.exists(meta.path)) stop("no cell_metadata.csv in ", DATASET) +md <- data.table::fread(meta.path, colClasses = list(character = 1)) +names(md)[1] <- "cell_id" +for (need in c("center_x", "center_y")) + if (!need %in% names(md)) stop("missing column '", need, "' in cell_metadata.csv") + +meta.data <- data.frame( + x = as.numeric(md$center_x), # microns already — no conversion + y = as.numeric(md$center_y), + row.names = as.character(md$cell_id), + stringsAsFactors = FALSE +) +cat(" coordinates already in microns — no conversion\n") +report_extent(meta.data, "um") + +aligned <- align_counts_meta(count.mtx, meta.data) +count.mtx <- aligned$count.mtx +meta.data <- aligned$meta.data + +if (MINCOUNT > 0) { + keep <- Matrix::colSums(count.mtx) >= MINCOUNT + cat(sprintf(" dropping %d cells with < %d counts (%d remain)\n", + sum(!keep), MINCOUNT, sum(keep))) + count.mtx <- count.mtx[, keep, drop = FALSE] + meta.data <- meta.data[keep, , drop = FALSE] +} + +# ── 3. Build and export ─────────────────────────────────────────────────────── +# Fails early and legibly when the panel carries no complete LR pair, which is +# the usual outcome for a small targeted panel. +check_lr_coverage(count.mtx, SPECIES) + +cat("\nrunning NICHESv2 (spatial mode)…\n") +obj <- create_NICHESObject( + count.mtx = count.mtx, + meta.data = meta.data, + LRM.db = "connectomedb2025", + species = SPECIES, + mode = "spatial", + rad = RAD, # microns, matching meta.data$x/$y + method = "product", + normalize.method = "prop", + n.cores = CORES, + verbose = TRUE +) + +cat("\nexporting…\n") +export_to_TissuePlex(obj, output.path = OUT, + x.col = "x", y.col = "y", + celltype.col = NULL, n.threads = CORES) + +validate_edges_parquet(OUT) +cat("\ndone — reload TissuePlex to see the edge layer.\n") diff --git a/r/niches_visium_hd.R b/r/niches_visium_hd.R index e2cb5a0..4d24e63 100755 --- a/r/niches_visium_hd.R +++ b/r/niches_visium_hd.R @@ -19,10 +19,19 @@ ## NICHESv2 ever sees the coordinates. `rad` is then in microns too, and ## comparable to the value you would use on Xenium. ## -## Bins are not cells. A 16 um bin may contain several cells or none, so treat +## The bin you pick must match the bin TissuePlex displays +## -------------------------------------------------------- +## Barcodes are bin-size specific — `s_008um_00172_00043-1` and +## `s_016um_00066_00065-1` name different things. visium_hd_reader.py serves +## `square_008um` by default (Space Ranger's own analysis default, and +## spatialdata-io's DEFAULT_BIN), so edges generated at another bin size render +## from their own coordinates but join to nothing: clicking a bin finds no edge, +## the cell filter drops every edge, and the tissue graph is disconnected from +## the bins under it. The default here matches the reader for that reason. +## +## Bins are not cells. An 8 um bin may contain a cell, several, or none, so treat ## "communication" between bins as a neighbourhood statistic rather than a -## cell-cell claim. Prefer the coarser bins: 2 um bins are mostly empty and there -## are millions of them. +## cell-cell claim. Avoid 2 um bins: mostly empty, and there are millions. ## ## Inputs ## binned_outputs//filtered_feature_bc_matrix.h5 @@ -50,12 +59,12 @@ positional <- args[!grepl("^--", args) & !(seq_along(args) %in% (which(grepl("^--", args)) + 1L))] DATASET <- if (length(positional)) positional[1] else "sample_data/visium_hd_tiny" -BIN <- opt("--bin", "square_016um") # coarser = fewer, denser bins +BIN <- opt("--bin", "square_008um") # MUST match the bin TissuePlex shows OUT <- opt("--out", file.path(DATASET, "edges.parquet")) SPECIES <- opt("--species", "mouse") # the 10x tiny demo is mouse brain -# rad in MICRONS. For 16 um bins, 40 um reaches the 8 immediate neighbours plus a -# little; scale it with the bin size if you switch bins. -RAD <- as.numeric(opt("--rad", "40")) +# rad in MICRONS. For 8 um bins, 24 um reaches the immediate neighbours plus a +# little; scale it with the bin size if you switch bins (40 um suited 16 um bins). +RAD <- as.numeric(opt("--rad", "24")) CORES <- as.integer(opt("--cores", "4")) MINCOUNT <- as.integer(opt("--min-counts", "1")) # drop empty bins diff --git a/sample_data/make_edge_metadata.py b/sample_data/make_edge_metadata.py new file mode 100644 index 0000000..f10e740 --- /dev/null +++ b/sample_data/make_edge_metadata.py @@ -0,0 +1,211 @@ +#!/usr/bin/env python3 +""" +Generate a demo ``edge-metadata/`` folder for any dataset that has edges. + +Why this exists +--------------- +The ``edge-metadata/`` convention lets a user annotate cell pairs — a call, a +confidence, a review flag — without regenerating ``edges.parquet`` from R. It is +easy to describe and hard to appreciate until you can click an edge and see the +annotation, so every bundled dataset gets one. + +What it writes, and how honest each column is +--------------------------------------------- +Three of the four columns are **derived from the edge data itself**, so they carry +real information and are reproducible from the parquet: + + interaction_class homotypic / heterotypic, from sending_type vs receiving_type, + or autocrine / paracrine when the edge file has no cell-type + columns. Not a column of edges.parquet, so it genuinely adds + something, but it is a restatement of that file rather than + a new measurement. + score_tier low / mid / high, by tercile of the edge's summed score. + score_percentile 0-100, the edge's rank by summed score. Continuous, so the + edge colour-by has a gradient and clamp sliders to exercise + — every other column here is categorical. + +One is **invented**, and is named so that it cannot be mistaken for anything else: + + demo_review_flag reviewed / unreviewed, assigned by a seeded hash of the edge + id. It exists purely to give the categorical edge filter and + the edge colour-by something to demonstrate. + +Every folder also gets a README.md saying the same thing, because a stray CSV of +plausible-looking annotations sitting next to real data is a hazard: someone will +eventually read ``score_tier: high`` on a real kidney section as an analysis +result. Nothing here is an analysis result. + +Usage +----- + python make_edge_metadata.py # every dataset with edges + python make_edge_metadata.py --dataset visium_tiny + python make_edge_metadata.py --force # overwrite existing folders + +Writes /edge-metadata/{example_annotations.csv, README.md}. +""" +from __future__ import annotations + +import argparse +import hashlib +from pathlib import Path + +import pandas as pd + +# Deterministic, so re-running never churns the file or the golden baseline. +SEED = "tissueplex-demo" + +README = """# edge-metadata/ — generated demo annotations + +**Not analysis output.** Generated by `sample_data/make_edge_metadata.py`; +reproduce with: + +```bash +python sample_data/make_edge_metadata.py --dataset {dataset} --force +``` + +This folder exists so the `edge-metadata/` feature has something to show on every +bundled dataset — the columns appear in the edge colour-by dropdown, in the edge +filter, and as an *Annotations* block when you click an edge. + +| Column | Where it comes from | +|---|---| +| `interaction_class` | Derived from the edge file: {class_basis}. | +| `score_tier` | Derived from the edge file: tercile of the edge's summed score. | +| `score_percentile` | Derived from the edge file: percentile rank of the summed score. The one continuous column. | +| `demo_review_flag` | **Invented.** A seeded hash of the edge id, nothing more. | + +The first three restate `edges.parquet` in a form it does not itself carry, so they +are reproducible and true to the data. The last is fabricated and named to make +that obvious. Do not read biology into any of it. +""" + + +def edge_sources(dataset: Path) -> list[Path]: + """Every edge parquet for a dataset: the legacy top-level file and edges/.""" + out = [] + top = dataset / "edges.parquet" + if top.exists(): + out.append(top) + sub = dataset / "edges" + if sub.is_dir(): + out.extend(sorted(sub.glob("*.parquet"))) + return out + + +def build(dataset: Path, force: bool) -> str | None: + sources = edge_sources(dataset) + if not sources: + return None + out_dir = dataset / "edge-metadata" + if out_dir.exists() and not force: + return f"skipped (already has edge-metadata/, use --force)" + + # Annotations are keyed on `edge` and apply across every edge source in the + # dataset — they describe cell pairs, which belong to the tissue rather than + # to one scoring run. So pool the edge ids from all sources. + import pyarrow.parquet as pq + + want = ["edge", "score", "is_autocrine", "sending_type", "receiving_type"] + frames = [] + for src in sources: + # Read only the columns that exist, and only those: a real edge file is + # millions of rows, so pulling all sixteen columns to use four is waste. + take = [c for c in want if c in set(pq.read_schema(src).names)] + if "edge" not in take: + continue + frames.append(pd.read_parquet(src, columns=take)) + if not frames: + return "skipped (no `edge` column in any edge source)" + + df = pd.concat(frames, ignore_index=True) + df = df[df["edge"].notna()] + if df.empty: + return "skipped (no edges)" + + # One row per edge. Sum the score so the tier reflects the whole edge, not a + # single LRM row. + agg = {"score": "sum"} if "score" in df.columns else {} + for c in ("is_autocrine", "sending_type", "receiving_type"): + if c in df.columns: + agg[c] = "first" + grouped = df.groupby("edge", as_index=False).agg(agg) if agg \ + else df[["edge"]].drop_duplicates() + + # ── interaction_class ──────────────────────────────────────────────────── + has_types = {"sending_type", "receiving_type"} <= set(grouped.columns) and \ + grouped["sending_type"].notna().any() + if has_types: + grouped["interaction_class"] = [ + "homotypic" if s == r else "heterotypic" + for s, r in zip(grouped["sending_type"], grouped["receiving_type"]) + ] + class_basis = "`homotypic` when sending_type == receiving_type, else `heterotypic`" + elif "is_autocrine" in grouped.columns: + grouped["interaction_class"] = [ + "autocrine" if bool(a) else "paracrine" for a in grouped["is_autocrine"] + ] + class_basis = ("`autocrine` / `paracrine` from the is_autocrine flag " + "(this edge file carries no cell-type columns)") + else: + grouped["interaction_class"] = "unclassified" + class_basis = "constant — this edge file carries neither cell types nor is_autocrine" + + # ── score_tier ─────────────────────────────────────────────────────────── + if "score" in grouped.columns and grouped["score"].notna().any(): + try: + grouped["score_tier"] = pd.qcut( + grouped["score"].rank(method="first"), 3, + labels=["low", "mid", "high"] + ).astype(str) + except ValueError: # too few distinct values to cut into three + grouped["score_tier"] = "mid" + grouped["score_percentile"] = ( + grouped["score"].rank(pct=True) * 100).round(2) + else: + grouped["score_tier"] = "unscored" + grouped["score_percentile"] = 0.0 + + # ── demo_review_flag — invented, and named to say so ───────────────────── + grouped["demo_review_flag"] = [ + "reviewed" if int(hashlib.sha256((SEED + str(e)).encode()).hexdigest(), 16) % 3 == 0 + else "unreviewed" + for e in grouped["edge"] + ] + + out_dir.mkdir(parents=True, exist_ok=True) + out = grouped[["edge", "interaction_class", "score_tier", + "score_percentile", "demo_review_flag"]] + # index=False plus an explicit `edge` column: the loader resolves the key as + # an explicit `edge` column first, so this needs no unnamed rowname column. + out.to_csv(out_dir / "example_annotations.csv", index=False) + (out_dir / "README.md").write_text( + README.format(dataset=dataset.name, class_basis=class_basis)) + + counts = out["interaction_class"].value_counts().to_dict() + return f"{len(out):>7,} edges {counts}" + + +def main() -> int: + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--dataset", help="one dataset name; default is all with edges") + ap.add_argument("--root", default=str(Path(__file__).resolve().parent)) + ap.add_argument("--force", action="store_true", help="overwrite existing folders") + args = ap.parse_args() + + root = Path(args.root) + targets = [root / args.dataset] if args.dataset else \ + sorted(p for p in root.iterdir() if p.is_dir()) + + for d in targets: + if not d.is_dir(): + print(f" {d.name}: not a directory") + continue + result = build(d, args.force) + if result is not None: + print(f" {d.name:26s} {result}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/sample_data/make_edges.py b/sample_data/make_edges.py index 6928192..0dcc55f 100644 --- a/sample_data/make_edges.py +++ b/sample_data/make_edges.py @@ -73,12 +73,46 @@ def _score_for_pair(dist: float, rng: random.Random, np_rng) -> float: return float(np.clip(base + rng.gauss(0, 0.35), 0.01, 4.0)) +def _load_cells(dataset_dir: Path) -> pd.DataFrame: + """Cell ids and centroids in **microns**, for any supported platform. + + Xenium's `cells.parquet` is read directly when present, since that keeps this + script usable with no backend on the path. Otherwise it falls back to the + TissuePlex reader, which is what makes the generator work on seqFISH, CosMx, + MERSCOPE and the two Visium platforms. + + The units conversion is the part to get right. Readers return centroids in + **image pixel space**, while `edges.parquet` stores **microns** — the backend + divides by `pixel_size` to place edges. So the reader path multiplies back up + by `pixel_size`, and the round trip lands the edge exactly on its cell. + """ + xen = dataset_dir / "cells.parquet" + if xen.exists(): + return pd.read_parquet(xen)[["cell_id", "x_centroid", "y_centroid"]] + + import sys + sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "backend")) + from app.readers.reader_factory import ReaderFactory + + reader = ReaderFactory.detect(dataset_dir) + ps = reader.pixel_size + rows = reader.cells() + if not rows: + raise SystemExit(f"{dataset_dir.name}: reader returned no cells") + df = pd.DataFrame(rows)[["cell_id", "x_centroid", "y_centroid"]].dropna() + df["x_centroid"] = df["x_centroid"].astype(float) * ps # image px → µm + df["y_centroid"] = df["y_centroid"].astype(float) * ps + print(f" read {len(df)} cells via the {reader.platform} reader " + f"(pixel_size {ps:.5f} µm/px)") + return df.reset_index(drop=True) + + def make_edges(dataset_dir: Path, k_neighbors: int = 6, lrms_per_pair: int = 3, autocrine_fraction: float = 0.15, seed: int = 42) -> pd.DataFrame: rng = random.Random(seed) np_rng = np.random.default_rng(seed) - cells = pd.read_parquet(dataset_dir / "cells.parquet")[["cell_id", "x_centroid", "y_centroid"]] + cells = _load_cells(dataset_dir) coords = cells[["x_centroid", "y_centroid"]].values # (N, 2) µm N = len(cells) barcodes = cells["cell_id"].tolist() diff --git a/sample_data/mouse_ileum_tiny/edge-metadata/README.md b/sample_data/mouse_ileum_tiny/edge-metadata/README.md new file mode 100644 index 0000000..fbb9fe4 --- /dev/null +++ b/sample_data/mouse_ileum_tiny/edge-metadata/README.md @@ -0,0 +1,23 @@ +# edge-metadata/ — generated demo annotations + +**Not analysis output.** Generated by `sample_data/make_edge_metadata.py`; +reproduce with: + +```bash +python sample_data/make_edge_metadata.py --dataset mouse_ileum_tiny --force +``` + +This folder exists so the `edge-metadata/` feature has something to show on every +bundled dataset — the columns appear in the edge colour-by dropdown, in the edge +filter, and as an *Annotations* block when you click an edge. + +| Column | Where it comes from | +|---|---| +| `interaction_class` | Derived from the edge file: `homotypic` when sending_type == receiving_type, else `heterotypic`. | +| `score_tier` | Derived from the edge file: tercile of the edge's summed score. | +| `score_percentile` | Derived from the edge file: percentile rank of the summed score. The one continuous column. | +| `demo_review_flag` | **Invented.** A seeded hash of the edge id, nothing more. | + +The first three restate `edges.parquet` in a form it does not itself carry, so they +are reproducible and true to the data. The last is fabricated and named to make +that obvious. Do not read biology into any of it. diff --git a/sample_data/mouse_ileum_tiny/edge-metadata/example_annotations.csv b/sample_data/mouse_ileum_tiny/edge-metadata/example_annotations.csv index fc81b14..a41ba7d 100644 --- a/sample_data/mouse_ileum_tiny/edge-metadata/example_annotations.csv +++ b/sample_data/mouse_ileum_tiny/edge-metadata/example_annotations.csv @@ -1,224 +1,308 @@ -,interaction_class,confidence,reviewed -aaclkaod-1|aaamobki-1,heterotypic,0.594,no -dgcpicgh-1|ehjojgpl-1,heterotypic,0.791,no -egeelggc-1|ckfandjp-1,homotypic,0.722,no -fkhfgimb-1|gpihhicj-1,heterotypic,0.435,yes -gackndbe-1|egeelggc-1,heterotypic,0.422,no -hhmlkgel-1|hkhcepfl-1,heterotypic,0.442,no -jpbefbck-1|kecdfjaf-1,heterotypic,0.655,yes -lfenejfi-1|oinmeidp-1,homotypic,0.474,no -omjmdimk-1|jdchjdgi-1,homotypic,0.776,yes -omjmdimk-1|ojeggnjp-1,heterotypic,0.746,no -kkonlcio-1|kkonlcio-1,homotypic,0.986,no -ckfandjp-1|aaclkaod-1,heterotypic,0.915,no -egeelggc-1|dkcgpkmh-1,heterotypic,0.487,no -hkhcepfl-1|niehkpen-1,heterotypic,0.585,yes -jpbefbck-1|jdchjdgi-1,homotypic,0.508,yes -kecdfjaf-1|jedneilm-1,heterotypic,0.783,no -kecdfjaf-1|jpbefbck-1,heterotypic,0.729,no -kfefndgm-1|jdchjdgi-1,heterotypic,0.436,no -kfefndgm-1|kggmilif-1,heterotypic,0.808,no -kfefndgm-1|kkonlcio-1,homotypic,0.588,yes -koaolmni-1|hbpoaeic-1,heterotypic,0.672,no -lfenejfi-1|niehkpen-1,homotypic,0.877,yes -oinmeidp-1|koaolmni-1,heterotypic,0.546,yes -omjmdimk-1|kfefndgm-1,heterotypic,0.715,yes -omjmdimk-1|olbjkpjc-1,heterotypic,0.838,no -kggmilif-1|kggmilif-1,homotypic,0.988,no -aaamobki-1|ckfandjp-1,homotypic,0.651,yes -bhakoonb-1|gackndbe-1,heterotypic,0.491,no -bhakoonb-1|gjklkjjk-1,heterotypic,0.424,yes -bpefijoo-1|aaclkaod-1,heterotypic,0.859,yes -bpefijoo-1|bhakoonb-1,heterotypic,0.925,no -ckfandjp-1|egeelggc-1,homotypic,0.817,yes -dgcpicgh-1|ckfandjp-1,heterotypic,0.748,no -dgcpicgh-1|egeelggc-1,heterotypic,0.904,yes -egeelggc-1|gackndbe-1,heterotypic,0.684,yes -fkhfgimb-1|gjklkjjk-1,heterotypic,0.436,yes -gackndbe-1|ehjojgpl-1,heterotypic,0.788,yes -ghfdfbpc-1|ehjojgpl-1,heterotypic,0.893,no -gjklkjjk-1|ckfandjp-1,heterotypic,0.631,yes -hakcibka-1|hhmlkgel-1,heterotypic,0.414,no -hakcibka-1|ohmibdle-1,heterotypic,0.501,no -ibclpflk-1|ohmibdle-1,heterotypic,0.435,yes -ifjkhhkf-1|kggmilif-1,heterotypic,0.478,no -jedneilm-1|kggmilif-1,heterotypic,0.635,yes -kfefndgm-1|kecdfjaf-1,heterotypic,0.448,no -kggmilif-1|kecdfjaf-1,heterotypic,0.73,yes -kggmilif-1|olbjkpjc-1,heterotypic,0.892,yes -niehkpen-1|hbpoaeic-1,heterotypic,0.567,no -niehkpen-1|oinmeidp-1,homotypic,0.615,yes -oinmeidp-1|hbpoaeic-1,heterotypic,0.975,no -dgcpicgh-1|gackndbe-1,heterotypic,0.506,no -egeelggc-1|ehjojgpl-1,homotypic,0.54,no -gjklkjjk-1|ghfdfbpc-1,heterotypic,0.753,no -hbpoaeic-1|koaolmni-1,heterotypic,0.402,no -hhmlkgel-1|ibclpflk-1,homotypic,0.622,yes -hhmlkgel-1|koaolmni-1,heterotypic,0.972,yes -hkhcepfl-1|ibclpflk-1,heterotypic,0.709,yes -ibclpflk-1|niehkpen-1,homotypic,0.806,no -ifjkhhkf-1|kfefndgm-1,heterotypic,0.94,yes -kfefndgm-1|ifjkhhkf-1,heterotypic,0.925,yes -lfenejfi-1|koaolmni-1,heterotypic,0.635,no -niehkpen-1|hhmlkgel-1,homotypic,0.462,yes -oinmeidp-1|lfenejfi-1,homotypic,0.437,no -ibclpflk-1|ibclpflk-1,homotypic,0.525,no -bhakoonb-1|aaamobki-1,homotypic,0.604,no -ckfandjp-1|ehjojgpl-1,homotypic,0.4,no -djgiipfb-1|ehjojgpl-1,heterotypic,0.461,no -ehjojgpl-1|egeelggc-1,homotypic,0.415,yes -fkhfgimb-1|gackndbe-1,heterotypic,0.768,no -ghfdfbpc-1|gjklkjjk-1,heterotypic,0.551,no -ghfdfbpc-1|gpihhicj-1,heterotypic,0.618,no -gpihhicj-1|bhakoonb-1,heterotypic,0.909,yes -hkhcepfl-1|hhmlkgel-1,heterotypic,0.68,no -ibclpflk-1|hbpoaeic-1,heterotypic,0.452,no -ifjkhhkf-1|kecdfjaf-1,homotypic,0.606,no -jdchjdgi-1|kfefndgm-1,heterotypic,0.897,no -koaolmni-1|ibclpflk-1,heterotypic,0.414,yes -aaclkaod-1|ckfandjp-1,heterotypic,0.717,no -bhakoonb-1|fkhfgimb-1,heterotypic,0.726,no -bpefijoo-1|ckfandjp-1,heterotypic,0.717,yes -dkcgpkmh-1|ckfandjp-1,heterotypic,0.918,yes -dkcgpkmh-1|ehjojgpl-1,heterotypic,0.557,no -gpihhicj-1|ckfandjp-1,heterotypic,0.5,yes -gpihhicj-1|fkhfgimb-1,heterotypic,0.72,yes -gpihhicj-1|gackndbe-1,homotypic,0.598,no -gpihhicj-1|ghfdfbpc-1,heterotypic,0.887,yes -gpihhicj-1|gjklkjjk-1,heterotypic,0.912,yes -hakcibka-1|koaolmni-1,heterotypic,0.891,yes -jpbefbck-1|kfefndgm-1,heterotypic,0.536,yes -kkonlcio-1|kfefndgm-1,homotypic,0.613,no -koaolmni-1|ohmibdle-1,heterotypic,0.417,no -lfenejfi-1|ohmibdle-1,heterotypic,0.556,yes -oinmeidp-1|ohmibdle-1,heterotypic,0.974,no -ojeggnjp-1|olbjkpjc-1,heterotypic,0.962,yes -olbjkpjc-1|omjmdimk-1,heterotypic,0.973,no -bpefijoo-1|ehjojgpl-1,heterotypic,0.532,no -dkcgpkmh-1|dgcpicgh-1,heterotypic,0.518,no -ghfdfbpc-1|gackndbe-1,heterotypic,0.774,yes -hakcibka-1|niehkpen-1,heterotypic,0.904,no -ifjkhhkf-1|kkonlcio-1,heterotypic,0.792,yes -jdchjdgi-1|kecdfjaf-1,heterotypic,0.451,yes -jedneilm-1|jpbefbck-1,heterotypic,0.946,yes 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+olbjkpjc-1|ojeggnjp-1,heterotypic,mid,59.61,unreviewed +olbjkpjc-1|olbjkpjc-1,homotypic,low,1.3,unreviewed +olbjkpjc-1|omjmdimk-1,heterotypic,high,91.21,reviewed +omjmdimk-1|jdchjdgi-1,homotypic,high,99.02,unreviewed +omjmdimk-1|jedneilm-1,heterotypic,low,20.52,unreviewed +omjmdimk-1|kecdfjaf-1,heterotypic,mid,37.13,reviewed +omjmdimk-1|kfefndgm-1,heterotypic,mid,38.76,reviewed +omjmdimk-1|kggmilif-1,heterotypic,high,93.16,reviewed +omjmdimk-1|kkonlcio-1,homotypic,low,5.21,unreviewed +omjmdimk-1|ojeggnjp-1,heterotypic,high,86.32,reviewed +omjmdimk-1|olbjkpjc-1,heterotypic,mid,61.24,reviewed diff --git a/sample_data/seqfish_synthetic/edge-metadata/README.md b/sample_data/seqfish_synthetic/edge-metadata/README.md new file mode 100644 index 0000000..d0fe0fe --- /dev/null +++ b/sample_data/seqfish_synthetic/edge-metadata/README.md @@ -0,0 +1,23 @@ +# edge-metadata/ — generated demo annotations + +**Not analysis output.** Generated by `sample_data/make_edge_metadata.py`; +reproduce with: + +```bash +python sample_data/make_edge_metadata.py --dataset seqfish_synthetic --force +``` + +This folder exists so the `edge-metadata/` feature has something to show on every +bundled dataset — the columns appear in the edge colour-by dropdown, in the edge +filter, and as an *Annotations* block when you click an edge. + +| Column | Where it comes from | +|---|---| +| `interaction_class` | Derived from the edge file: `autocrine` / `paracrine` from the is_autocrine flag (this edge file carries no cell-type columns). | +| `score_tier` | Derived from the edge file: tercile of the edge's summed score. | +| `score_percentile` | Derived from the edge file: percentile rank of the summed score. The one continuous column. | +| `demo_review_flag` | **Invented.** A seeded hash of the edge id, nothing more. | + +The first three restate `edges.parquet` in a form it does not itself carry, so they +are reproducible and true to the data. The last is fabricated and named to make +that obvious. Do not read biology into any of it. diff --git a/sample_data/seqfish_synthetic/edge-metadata/example_annotations.csv b/sample_data/seqfish_synthetic/edge-metadata/example_annotations.csv new file mode 100644 index 0000000..352cae3 --- /dev/null +++ b/sample_data/seqfish_synthetic/edge-metadata/example_annotations.csv @@ -0,0 +1,251 @@ +edge,interaction_class,score_tier,score_percentile,demo_review_flag +10|10,autocrine,low,22.8,reviewed +10|11,paracrine,mid,39.0,unreviewed +10|15,paracrine,low,32.8,unreviewed +10|16,paracrine,mid,35.2,reviewed +10|17,paracrine,mid,51.6,unreviewed +10|3,paracrine,low,20.8,reviewed +10|4,paracrine,low,29.6,unreviewed +10|5,paracrine,low,32.4,unreviewed +10|9,paracrine,low,11.0,unreviewed +11|10,paracrine,mid,49.6,unreviewed +11|11,autocrine,high,68.0,unreviewed +11|12,paracrine,high,68.4,unreviewed +11|16,paracrine,mid,60.0,unreviewed +11|17,paracrine,high,84.8,unreviewed +11|18,paracrine,high,75.6,unreviewed +11|5,paracrine,mid,54.8,unreviewed +11|6,paracrine,mid,56.0,unreviewed +12|11,paracrine,mid,52.8,reviewed +12|12,autocrine,mid,50.4,unreviewed +12|17,paracrine,mid,65.2,unreviewed +12|18,paracrine,mid,57.6,reviewed +12|5,paracrine,mid,39.6,reviewed +12|6,paracrine,mid,40.0,reviewed +13|13,autocrine,low,10.4,unreviewed +13|14,paracrine,low,19.6,reviewed +13|19,paracrine,low,6.8,reviewed +13|20,paracrine,low,26.8,unreviewed +13|7,paracrine,low,11.0,reviewed +13|8,paracrine,low,15.6,reviewed +14|13,paracrine,low,8.8,unreviewed +14|14,autocrine,low,13.2,unreviewed +14|15,paracrine,mid,41.2,unreviewed +14|19,paracrine,low,6.4,unreviewed +14|20,paracrine,low,25.6,unreviewed +14|21,paracrine,mid,36.8,unreviewed +14|7,paracrine,low,9.4,unreviewed +14|8,paracrine,low,11.6,unreviewed +14|9,paracrine,low,16.4,unreviewed +15|10,paracrine,mid,43.2,unreviewed +15|14,paracrine,low,31.6,reviewed +15|15,autocrine,mid,53.6,unreviewed +15|16,paracrine,mid,56.4,reviewed +15|20,paracrine,mid,47.2,unreviewed +15|21,paracrine,mid,58.8,unreviewed +15|22,paracrine,mid,62.4,unreviewed +15|8,paracrine,low,29.2,reviewed +15|9,paracrine,low,28.4,unreviewed +16|10,paracrine,mid,48.8,unreviewed +16|11,paracrine,high,70.0,unreviewed +16|15,paracrine,mid,64.0,unreviewed +16|16,autocrine,mid,63.6,unreviewed +16|17,paracrine,high,82.0,reviewed +16|21,paracrine,mid,65.6,unreviewed +16|22,paracrine,high,66.8,reviewed +16|23,paracrine,high,77.6,unreviewed +16|9,paracrine,mid,36.0,reviewed +17|10,paracrine,mid,55.2,reviewed +17|11,paracrine,high,79.2,unreviewed +17|12,paracrine,high,73.6,reviewed +17|16,paracrine,high,68.8,reviewed +17|17,autocrine,high,92.8,reviewed +17|18,paracrine,high,82.4,reviewed +17|22,paracrine,high,75.2,unreviewed +17|23,paracrine,high,84.0,reviewed +17|24,paracrine,high,87.2,unreviewed +18|11,paracrine,high,76.4,reviewed +18|12,paracrine,high,71.6,unreviewed +18|17,paracrine,high,92.0,reviewed +18|18,autocrine,high,83.2,unreviewed +18|23,paracrine,high,80.8,unreviewed +18|24,paracrine,high,86.8,unreviewed +19|13,paracrine,low,16.8,unreviewed +19|14,paracrine,low,24.8,unreviewed +19|19,autocrine,low,18.4,unreviewed +19|20,paracrine,mid,38.4,unreviewed +19|25,paracrine,mid,34.0,reviewed +19|26,paracrine,low,28.8,reviewed +1|1,autocrine,low,4.4,unreviewed +1|2,paracrine,low,3.2,unreviewed +1|7,paracrine,low,2.4,reviewed +1|8,paracrine,low,3.6,unreviewed +20|13,paracrine,low,14.0,unreviewed +20|14,paracrine,low,20.4,reviewed +20|15,paracrine,mid,51.2,reviewed +20|19,paracrine,low,13.6,unreviewed +20|20,autocrine,mid,34.4,unreviewed +20|21,paracrine,mid,45.8,unreviewed +20|25,paracrine,mid,34.8,reviewed +20|26,paracrine,low,21.6,unreviewed +20|27,paracrine,mid,46.8,reviewed +21|14,paracrine,low,30.2,unreviewed +21|15,paracrine,mid,54.4,unreviewed +21|16,paracrine,mid,54.0,unreviewed +21|20,paracrine,mid,47.6,reviewed +21|21,autocrine,mid,55.6,unreviewed +21|22,paracrine,mid,59.6,reviewed +21|26,paracrine,mid,40.8,reviewed 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+8|1,paracrine,low,10.0,unreviewed +8|13,paracrine,low,8.0,unreviewed +8|14,paracrine,low,12.8,unreviewed +8|15,paracrine,mid,35.6,unreviewed +8|2,paracrine,low,6.0,unreviewed +8|3,paracrine,low,18.0,reviewed +8|7,paracrine,low,5.6,unreviewed +8|8,autocrine,low,9.4,unreviewed +8|9,paracrine,low,7.2,unreviewed +9|10,paracrine,low,32.0,unreviewed +9|14,paracrine,low,22.0,reviewed +9|15,paracrine,mid,44.2,reviewed +9|16,paracrine,mid,42.8,unreviewed +9|2,paracrine,low,16.0,reviewed +9|3,paracrine,low,26.0,reviewed +9|4,paracrine,mid,40.4,unreviewed +9|8,paracrine,low,17.2,unreviewed +9|9,autocrine,low,19.0,reviewed diff --git a/sample_data/visium_hd_tiny/edge-metadata/README.md b/sample_data/visium_hd_tiny/edge-metadata/README.md new file mode 100644 index 0000000..2c31d75 --- /dev/null +++ b/sample_data/visium_hd_tiny/edge-metadata/README.md @@ -0,0 +1,23 @@ +# edge-metadata/ — generated demo annotations + +**Not analysis output.** Generated by `sample_data/make_edge_metadata.py`; +reproduce with: + +```bash +python sample_data/make_edge_metadata.py --dataset visium_hd_tiny --force +``` + +This folder exists so the `edge-metadata/` feature has something to show on every +bundled dataset — the columns appear in the edge colour-by dropdown, in the edge +filter, and as an *Annotations* block when you click an edge. + +| Column | Where it comes from | +|---|---| +| `interaction_class` | Derived from the edge file: `autocrine` / `paracrine` from the is_autocrine flag (this edge file carries no cell-type columns). | +| `score_tier` | Derived from the edge file: tercile of the edge's summed score. | +| `score_percentile` | Derived from the edge file: percentile rank of the summed score. The one continuous column. | +| `demo_review_flag` | **Invented.** A seeded hash of the edge id, nothing more. | + +The first three restate `edges.parquet` in a form it does not itself carry, so they +are reproducible and true to the data. The last is fabricated and named to make +that obvious. 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+s_016um_00089_00009-1|s_016um_00088_00009-1,paracrine,high,49.89,unreviewed +s_016um_00089_00009-1|s_016um_00088_00010-1,paracrine,high,49.89,unreviewed +s_016um_00089_00009-1|s_016um_00088_00011-1,paracrine,high,49.89,unreviewed +s_016um_00089_00009-1|s_016um_00089_00007-1,paracrine,high,49.89,unreviewed +s_016um_00089_00009-1|s_016um_00089_00008-1,paracrine,high,49.89,unreviewed +s_016um_00089_00009-1|s_016um_00089_00009-1,autocrine,high,49.89,reviewed diff --git a/sample_data/visium_tiny/edge-metadata/README.md b/sample_data/visium_tiny/edge-metadata/README.md new file mode 100644 index 0000000..fbd26a1 --- /dev/null +++ b/sample_data/visium_tiny/edge-metadata/README.md @@ -0,0 +1,23 @@ +# edge-metadata/ — generated demo annotations + +**Not analysis output.** Generated by `sample_data/make_edge_metadata.py`; +reproduce with: + +```bash +python sample_data/make_edge_metadata.py --dataset visium_tiny --force +``` + +This folder exists so the `edge-metadata/` feature has something to show on every +bundled dataset — the columns appear in the edge colour-by dropdown, in the edge +filter, and as an *Annotations* block when you click an edge. + +| Column | Where it comes from | +|---|---| +| `interaction_class` | Derived from the edge file: `autocrine` / `paracrine` from the is_autocrine flag (this edge file carries no cell-type columns). | +| `score_tier` | Derived from the edge file: tercile of the edge's summed score. | +| `score_percentile` | Derived from the edge file: percentile rank of the summed score. The one continuous column. | +| `demo_review_flag` | **Invented.** A seeded hash of the edge id, nothing more. | + +The first three restate `edges.parquet` in a form it does not itself carry, so they +are reproducible and true to the data. The last is fabricated and named to make +that obvious. Do not read biology into any of it. diff --git a/sample_data/visium_tiny/edge-metadata/example_annotations.csv b/sample_data/visium_tiny/edge-metadata/example_annotations.csv new file mode 100644 index 0000000..d94256c --- /dev/null +++ b/sample_data/visium_tiny/edge-metadata/example_annotations.csv @@ -0,0 +1,1643 @@ +edge,interaction_class,score_tier,score_percentile,demo_review_flag +AAAACAAAAAAAAAAA-1|AAAACAAAAAAAAAAA-1,autocrine,mid,65.65,unreviewed +AAAACAAAAAAAAAAA-1|ACGTAAAAAAAAAAAA-1,paracrine,low,12.12,unreviewed +AAAACAAAAAAAAAAA-1|ATCACAAAAAAAAAAA-1,paracrine,mid,44.03,reviewed +AAAACAAAAAAAAAAA-1|CAAACAAAAAAAAAAA-1,paracrine,mid,36.3,reviewed +AAAACAAAAAAAAAAA-1|CCGTAAAAAAAAAAAA-1,paracrine,mid,48.14,reviewed +AAAACAAAAAAAAAAA-1|CTCACAAAAAAAAAAA-1,paracrine,low,16.87,reviewed +AAACCAAAAAAAAAAA-1|AAACCAAAAAAAAAAA-1,autocrine,mid,63.25,unreviewed +AAACCAAAAAAAAAAA-1|ACGACAAAAAAAAAAA-1,paracrine,mid,52.89,unreviewed +AAACCAAAAAAAAAAA-1|ATCCCAAAAAAAAAAA-1,paracrine,high,89.89,unreviewed +AAACCAAAAAAAAAAA-1|CAACCAAAAAAAAAAA-1,paracrine,mid,61.11,reviewed +AAACCAAAAAAAAAAA-1|CCGACAAAAAAAAAAA-1,paracrine,mid,63.0,unreviewed +AAACCAAAAAAAAAAA-1|CTCCCAAAAAAAAAAA-1,paracrine,mid,48.14,reviewed +AAACCAAAAAAAAAAA-1|TTTACAAAAAAAAAAA-1,paracrine,mid,61.11,reviewed +AAAGAAAAAAAAAAAA-1|AAAGAAAAAAAAAAAA-1,autocrine,high,98.78,unreviewed +AAAGAAAAAAAAAAAA-1|ACGCAAAAAAAAAAAA-1,paracrine,low,5.3,unreviewed +AAAGAAAAAAAAAAAA-1|ATCGAAAAAAAAAAAA-1,paracrine,low,4.81,unreviewed +AAAGAAAAAAAAAAAA-1|CAAGAAAAAAAAAAAA-1,paracrine,low,5.24,unreviewed +AAAGAAAAAAAAAAAA-1|TAGCAAAAAAAAAAAA-1,paracrine,mid,58.25,unreviewed +AAAGAAAAAAAAAAAA-1|TGCGAAAAAAAAAAAA-1,paracrine,high,98.05,unreviewed +AAAGAAAAAAAAAAAA-1|TTTCAAAAAAAAAAAA-1,paracrine,high,80.45,unreviewed +AAAGCAAAAAAAAAAA-1|AAAGCAAAAAAAAAAA-1,autocrine,mid,37.52,unreviewed +AAAGCAAAAAAAAAAA-1|ACGCCAAAAAAAAAAA-1,paracrine,low,16.63,reviewed +AAAGCAAAAAAAAAAA-1|ATCGCAAAAAAAAAAA-1,paracrine,mid,62.24,unreviewed +AAAGCAAAAAAAAAAA-1|CAAGCAAAAAAAAAAA-1,paracrine,high,84.23,reviewed +AAAGCAAAAAAAAAAA-1|CCGCCAAAAAAAAAAA-1,paracrine,high,75.27,unreviewed +AAAGCAAAAAAAAAAA-1|CTCGCAAAAAAAAAAA-1,paracrine,mid,58.68,unreviewed +AAAGCAAAAAAAAAAA-1|TTTCCAAAAAAAAAAA-1,paracrine,low,22.02,unreviewed +AACACAAAAAAAAAAA-1|AACACAAAAAAAAAAA-1,autocrine,high,94.58,unreviewed +AACACAAAAAAAAAAA-1|ACTTAAAAAAAAAAAA-1,paracrine,high,68.3,unreviewed +AACACAAAAAAAAAAA-1|ATGACAAAAAAAAAAA-1,paracrine,high,90.93,unreviewed +AACACAAAAAAAAAAA-1|CACACAAAAAAAAAAA-1,paracrine,high,99.45,reviewed +AACACAAAAAAAAAAA-1|CCTTAAAAAAAAAAAA-1,paracrine,high,99.27,reviewed +AACACAAAAAAAAAAA-1|CTGACAAAAAAAAAAA-1,paracrine,high,98.66,reviewed +AACACAAAAAAAAAAA-1|TTAACAAAAAAAAAAA-1,paracrine,high,72.5,reviewed +AACGCAAAAAAAAAAA-1|AACGCAAAAAAAAAAA-1,autocrine,low,6.88,unreviewed +AACGCAAAAAAAAAAA-1|ACTCCAAAAAAAAAAA-1,paracrine,low,9.01,unreviewed 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+AACTCAAAAAAAAAAA-1|ATGTCAAAAAAAAAAA-1,paracrine,low,2.44,unreviewed +AACTCAAAAAAAAAAA-1|CACTCAAAAAAAAAAA-1,paracrine,low,1.95,reviewed +AACTCAAAAAAAAAAA-1|TATGCAAAAAAAAAAA-1,paracrine,high,95.01,unreviewed +AACTCAAAAAAAAAAA-1|TGGTCAAAAAAAAAAA-1,paracrine,low,11.08,unreviewed +AACTCAAAAAAAAAAA-1|TTATCAAAAAAAAAAA-1,paracrine,mid,49.97,reviewed +AAGACAAAAAAAAAAA-1|AAGACAAAAAAAAAAA-1,autocrine,low,21.74,unreviewed +AAGACAAAAAAAAAAA-1|ACAACAAAAAAAAAAA-1,paracrine,mid,54.32,unreviewed +AAGACAAAAAAAAAAA-1|ATTACAAAAAAAAAAA-1,paracrine,low,17.42,unreviewed +AAGACAAAAAAAAAAA-1|CAGACAAAAAAAAAAA-1,paracrine,mid,57.58,unreviewed +AAGACAAAAAAAAAAA-1|TAAACAAAAAAAAAAA-1,paracrine,low,32.1,unreviewed +AAGACAAAAAAAAAAA-1|TGTACAAAAAAAAAAA-1,paracrine,low,16.53,unreviewed +AAGACAAAAAAAAAAA-1|TTCACAAAAAAAAAAA-1,paracrine,mid,63.52,unreviewed +AAGCAAAAAAAAAAAA-1|AAGCAAAAAAAAAAAA-1,autocrine,mid,41.23,unreviewed +AAGCAAAAAAAAAAAA-1|ATTCAAAAAAAAAAAA-1,paracrine,mid,43.33,reviewed +AAGCAAAAAAAAAAAA-1|CAGCAAAAAAAAAAAA-1,paracrine,low,27.16,unreviewed +AAGCAAAAAAAAAAAA-1|CTTCAAAAAAAAAAAA-1,paracrine,mid,36.11,unreviewed +AAGCAAAAAAAAAAAA-1|TTCCAAAAAAAAAAAA-1,paracrine,low,17.84,unreviewed +AAGCCAAAAAAAAAAA-1|AAGCCAAAAAAAAAAA-1,autocrine,mid,66.57,reviewed +AAGCCAAAAAAAAAAA-1|ACACCAAAAAAAAAAA-1,paracrine,low,3.05,reviewed +AAGCCAAAAAAAAAAA-1|ATTCCAAAAAAAAAAA-1,paracrine,low,3.41,reviewed +AAGCCAAAAAAAAAAA-1|CAGCCAAAAAAAAAAA-1,paracrine,low,3.47,reviewed +AAGCCAAAAAAAAAAA-1|TAACCAAAAAAAAAAA-1,paracrine,mid,50.24,unreviewed +AAGCCAAAAAAAAAAA-1|TGTCCAAAAAAAAAAA-1,paracrine,mid,53.96,reviewed +AAGCCAAAAAAAAAAA-1|TTCCCAAAAAAAAAAA-1,paracrine,high,95.37,reviewed +AAGGAAAAAAAAAAAA-1|AAGGAAAAAAAAAAAA-1,autocrine,low,24.3,unreviewed +AAGGAAAAAAAAAAAA-1|ATTGAAAAAAAAAAAA-1,paracrine,high,73.87,unreviewed +AAGGAAAAAAAAAAAA-1|CTTGAAAAAAAAAAAA-1,paracrine,mid,65.93,unreviewed +AAGGAAAAAAAAAAAA-1|TTCGAAAAAAAAAAAA-1,paracrine,high,84.87,unreviewed 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+AGCCCAAAAAAAAAAA-1|ATTACAAAAAAAAAAA-1,paracrine,low,24.3,reviewed +AGCCCAAAAAAAAAAA-1|CGCCCAAAAAAAAAAA-1,paracrine,high,73.69,unreviewed +AGCCCAAAAAAAAAAA-1|TATCCAAAAAAAAAAA-1,paracrine,mid,60.9,unreviewed +AGCCCAAAAAAAAAAA-1|TCCCCAAAAAAAAAAA-1,paracrine,low,13.22,unreviewed +AGCCCAAAAAAAAAAA-1|TGTACAAAAAAAAAAA-1,paracrine,low,15.68,unreviewed +AGCGAAAAAAAAAAAA-1|ACTGAAAAAAAAAAAA-1,paracrine,high,84.01,reviewed +AGCGAAAAAAAAAAAA-1|AGCGAAAAAAAAAAAA-1,autocrine,high,94.15,reviewed +AGCGAAAAAAAAAAAA-1|ATTCAAAAAAAAAAAA-1,paracrine,high,84.01,unreviewed +AGCGAAAAAAAAAAAA-1|CCTGAAAAAAAAAAAA-1,paracrine,high,92.36,unreviewed +AGCGAAAAAAAAAAAA-1|CGCGAAAAAAAAAAAA-1,paracrine,high,97.14,reviewed +AGCGAAAAAAAAAAAA-1|CTTCAAAAAAAAAAAA-1,paracrine,high,84.47,unreviewed +AGCGAAAAAAAAAAAA-1|TCCGAAAAAAAAAAAA-1,paracrine,high,70.8,reviewed +AGCGCAAAAAAAAAAA-1|ACTGCAAAAAAAAAAA-1,paracrine,low,3.11,reviewed +AGCGCAAAAAAAAAAA-1|AGCGCAAAAAAAAAAA-1,autocrine,low,14.86,unreviewed +AGCGCAAAAAAAAAAA-1|ATTCCAAAAAAAAAAA-1,paracrine,low,4.02,reviewed +AGCGCAAAAAAAAAAA-1|CGCGCAAAAAAAAAAA-1,paracrine,low,3.23,unreviewed +AGCGCAAAAAAAAAAA-1|TATGCAAAAAAAAAAA-1,paracrine,high,88.55,unreviewed +AGCGCAAAAAAAAAAA-1|TCCGCAAAAAAAAAAA-1,paracrine,mid,64.34,unreviewed +AGCGCAAAAAAAAAAA-1|TGTCCAAAAAAAAAAA-1,paracrine,mid,45.13,reviewed +AGCTAAAAAAAAAAAA-1|ACTTAAAAAAAAAAAA-1,paracrine,mid,46.53,unreviewed +AGCTAAAAAAAAAAAA-1|AGCTAAAAAAAAAAAA-1,autocrine,mid,45.34,reviewed +AGCTAAAAAAAAAAAA-1|ATTGAAAAAAAAAAAA-1,paracrine,high,88.64,reviewed +AGCTAAAAAAAAAAAA-1|CCTTAAAAAAAAAAAA-1,paracrine,high,93.57,unreviewed +AGCTAAAAAAAAAAAA-1|CGCTAAAAAAAAAAAA-1,paracrine,high,78.29,reviewed +AGCTAAAAAAAAAAAA-1|CTTGAAAAAAAAAAAA-1,paracrine,high,82.31,reviewed +AGCTAAAAAAAAAAAA-1|TCCTAAAAAAAAAAAA-1,paracrine,high,69.34,reviewed +AGGACAAAAAAAAAAA-1|ACACCAAAAAAAAAAA-1,paracrine,low,4.2,reviewed +AGGACAAAAAAAAAAA-1|AGGACAAAAAAAAAAA-1,autocrine,mid,57.86,unreviewed +AGGACAAAAAAAAAAA-1|ATAACAAAAAAAAAAA-1,paracrine,low,4.38,unreviewed +AGGACAAAAAAAAAAA-1|CGGACAAAAAAAAAAA-1,paracrine,low,4.93,unreviewed +AGGACAAAAAAAAAAA-1|TAACCAAAAAAAAAAA-1,paracrine,mid,34.56,unreviewed +AGGACAAAAAAAAAAA-1|TCGACAAAAAAAAAAA-1,paracrine,mid,62.52,unreviewed +AGGACAAAAAAAAAAA-1|TGAACAAAAAAAAAAA-1,paracrine,mid,64.65,reviewed +AGGTAAAAAAAAAAAA-1|ACAACAAAAAAAAAAA-1,paracrine,low,19.91,unreviewed +AGGTAAAAAAAAAAAA-1|AGGTAAAAAAAAAAAA-1,autocrine,low,11.57,unreviewed +AGGTAAAAAAAAAAAA-1|ATATAAAAAAAAAAAA-1,paracrine,low,18.15,reviewed +AGGTAAAAAAAAAAAA-1|CGGTAAAAAAAAAAAA-1,paracrine,low,16.08,unreviewed +AGGTAAAAAAAAAAAA-1|TAAACAAAAAAAAAAA-1,paracrine,low,11.42,unreviewed +AGGTAAAAAAAAAAAA-1|TCGTAAAAAAAAAAAA-1,paracrine,low,17.3,reviewed +AGGTAAAAAAAAAAAA-1|TGATAAAAAAAAAAAA-1,paracrine,low,13.12,unreviewed +AGGTCAAAAAAAAAAA-1|ACAAGAAAAAAAAAAA-1,paracrine,low,30.72,unreviewed +AGGTCAAAAAAAAAAA-1|AGGTCAAAAAAAAAAA-1,autocrine,high,73.54,reviewed +AGGTCAAAAAAAAAAA-1|ATATCAAAAAAAAAAA-1,paracrine,mid,66.41,unreviewed +AGGTCAAAAAAAAAAA-1|CCAAGAAAAAAAAAAA-1,paracrine,mid,65.93,unreviewed +AGGTCAAAAAAAAAAA-1|CGGTCAAAAAAAAAAA-1,paracrine,high,93.18,unreviewed +AGGTCAAAAAAAAAAA-1|CTATCAAAAAAAAAAA-1,paracrine,high,95.22,unreviewed +AGGTCAAAAAAAAAAA-1|TCGTCAAAAAAAAAAA-1,paracrine,high,75.64,unreviewed +AGTACAAAAAAAAAAA-1|ACCCCAAAAAAAAAAA-1,paracrine,low,8.8,reviewed +AGTACAAAAAAAAAAA-1|AGTACAAAAAAAAAAA-1,autocrine,low,7.25,reviewed +AGTACAAAAAAAAAAA-1|ATCACAAAAAAAAAAA-1,paracrine,low,18.7,reviewed +AGTACAAAAAAAAAAA-1|CCCCCAAAAAAAAAAA-1,paracrine,low,20.37,reviewed +AGTACAAAAAAAAAAA-1|CGTACAAAAAAAAAAA-1,paracrine,low,12.61,reviewed +AGTACAAAAAAAAAAA-1|CTCACAAAAAAAAAAA-1,paracrine,low,7.98,unreviewed +AGTCAAAAAAAAAAAA-1|ACCGAAAAAAAAAAAA-1,paracrine,low,25.67,unreviewed +AGTCAAAAAAAAAAAA-1|AGTCAAAAAAAAAAAA-1,autocrine,low,25.06,unreviewed +AGTCAAAAAAAAAAAA-1|CGTCAAAAAAAAAAAA-1,paracrine,mid,36.91,reviewed +AGTCAAAAAAAAAAAA-1|TACGAAAAAAAAAAAA-1,paracrine,low,10.05,unreviewed +AGTCCAAAAAAAAAAA-1|ACCGCAAAAAAAAAAA-1,paracrine,high,98.42,reviewed +AGTCCAAAAAAAAAAA-1|AGTCCAAAAAAAAAAA-1,autocrine,high,99.09,unreviewed +AGTCCAAAAAAAAAAA-1|ATCCCAAAAAAAAAAA-1,paracrine,high,100.0,unreviewed +AGTCCAAAAAAAAAAA-1|CCCGCAAAAAAAAAAA-1,paracrine,high,82.89,reviewed +AGTCCAAAAAAAAAAA-1|CGTCCAAAAAAAAAAA-1,paracrine,high,99.94,unreviewed +AGTCCAAAAAAAAAAA-1|CTCCCAAAAAAAAAAA-1,paracrine,high,98.72,unreviewed +AGTCCAAAAAAAAAAA-1|TCTCCAAAAAAAAAAA-1,paracrine,high,98.33,unreviewed +AGTGAAAAAAAAAAAA-1|ACCTAAAAAAAAAAAA-1,paracrine,low,4.14,unreviewed +AGTGAAAAAAAAAAAA-1|AGTGAAAAAAAAAAAA-1,autocrine,high,81.49,unreviewed +AGTGAAAAAAAAAAAA-1|ATCGAAAAAAAAAAAA-1,paracrine,low,1.89,reviewed +AGTGAAAAAAAAAAAA-1|CGTGAAAAAAAAAAAA-1,paracrine,low,2.04,unreviewed +AGTGAAAAAAAAAAAA-1|TACTAAAAAAAAAAAA-1,paracrine,mid,39.46,unreviewed +AGTGAAAAAAAAAAAA-1|TCTGAAAAAAAAAAAA-1,paracrine,mid,65.29,unreviewed +AGTGAAAAAAAAAAAA-1|TGCGAAAAAAAAAAAA-1,paracrine,high,86.85,unreviewed +AGTGCAAAAAAAAAAA-1|ACCTCAAAAAAAAAAA-1,paracrine,mid,38.61,unreviewed +AGTGCAAAAAAAAAAA-1|AGTGCAAAAAAAAAAA-1,autocrine,low,8.5,reviewed +AGTGCAAAAAAAAAAA-1|ATCGCAAAAAAAAAAA-1,paracrine,mid,46.29,unreviewed +AGTGCAAAAAAAAAAA-1|CGTGCAAAAAAAAAAA-1,paracrine,low,19.73,unreviewed +AGTGCAAAAAAAAAAA-1|CTCGCAAAAAAAAAAA-1,paracrine,mid,40.04,reviewed +AGTGCAAAAAAAAAAA-1|TCTGCAAAAAAAAAAA-1,paracrine,low,33.13,unreviewed +ATAACAAAAAAAAAAA-1|AATTAAAAAAAAAAAA-1,paracrine,low,4.45,unreviewed +ATAACAAAAAAAAAAA-1|AGGACAAAAAAAAAAA-1,paracrine,low,6.58,unreviewed +ATAACAAAAAAAAAAA-1|ATAACAAAAAAAAAAA-1,autocrine,high,70.92,unreviewed +ATAACAAAAAAAAAAA-1|CATTAAAAAAAAAAAA-1,paracrine,mid,59.1,unreviewed +ATAACAAAAAAAAAAA-1|CGGACAAAAAAAAAAA-1,paracrine,high,71.41,unreviewed +ATAACAAAAAAAAAAA-1|CTAACAAAAAAAAAAA-1,paracrine,low,21.5,reviewed +ATAACAAAAAAAAAAA-1|TGAACAAAAAAAAAAA-1,paracrine,low,5.36,reviewed +ATATAAAAAAAAAAAA-1|AATGAAAAAAAAAAAA-1,paracrine,mid,37.61,reviewed +ATATAAAAAAAAAAAA-1|AGGTAAAAAAAAAAAA-1,paracrine,mid,41.6,unreviewed +ATATAAAAAAAAAAAA-1|ATATAAAAAAAAAAAA-1,autocrine,mid,62.7,unreviewed +ATATAAAAAAAAAAAA-1|CATGAAAAAAAAAAAA-1,paracrine,low,12.91,unreviewed +ATATAAAAAAAAAAAA-1|CGGTAAAAAAAAAAAA-1,paracrine,mid,60.05,unreviewed +ATATAAAAAAAAAAAA-1|CTATAAAAAAAAAAAA-1,paracrine,mid,61.54,unreviewed +ATATAAAAAAAAAAAA-1|TGATAAAAAAAAAAAA-1,paracrine,mid,47.14,unreviewed +ATATCAAAAAAAAAAA-1|AATGCAAAAAAAAAAA-1,paracrine,high,91.6,unreviewed +ATATCAAAAAAAAAAA-1|AGGTCAAAAAAAAAAA-1,paracrine,high,77.22,unreviewed +ATATCAAAAAAAAAAA-1|ATATCAAAAAAAAAAA-1,autocrine,mid,64.34,unreviewed +ATATCAAAAAAAAAAA-1|CTATCAAAAAAAAAAA-1,paracrine,high,93.79,reviewed +ATATCAAAAAAAAAAA-1|TCGTCAAAAAAAAAAA-1,paracrine,high,72.5,unreviewed +ATATCAAAAAAAAAAA-1|TGATCAAAAAAAAAAA-1,paracrine,high,90.86,unreviewed +ATATCAAAAAAAAAAA-1|TTGGCAAAAAAAAAAA-1,paracrine,high,92.69,unreviewed +ATCACAAAAAAAAAAA-1|AAAACAAAAAAAAAAA-1,paracrine,high,81.85,reviewed +ATCACAAAAAAAAAAA-1|AGTACAAAAAAAAAAA-1,paracrine,low,24.48,reviewed +ATCACAAAAAAAAAAA-1|ATCACAAAAAAAAAAA-1,autocrine,mid,61.33,unreviewed +ATCACAAAAAAAAAAA-1|CTCACAAAAAAAAAAA-1,paracrine,low,31.85,reviewed +ATCCCAAAAAAAAAAA-1|AAACCAAAAAAAAAAA-1,paracrine,high,95.07,reviewed +ATCCCAAAAAAAAAAA-1|AGTCCAAAAAAAAAAA-1,paracrine,high,95.98,reviewed +ATCCCAAAAAAAAAAA-1|ATCCCAAAAAAAAAAA-1,autocrine,high,99.57,unreviewed +ATCCCAAAAAAAAAAA-1|CTCCCAAAAAAAAAAA-1,paracrine,high,82.95,unreviewed +ATCCCAAAAAAAAAAA-1|TCTCCAAAAAAAAAAA-1,paracrine,high,81.55,reviewed +ATCCCAAAAAAAAAAA-1|TGCCCAAAAAAAAAAA-1,paracrine,high,93.91,unreviewed +ATCCCAAAAAAAAAAA-1|TTTACAAAAAAAAAAA-1,paracrine,high,93.03,unreviewed +ATCGAAAAAAAAAAAA-1|AAAGAAAAAAAAAAAA-1,paracrine,low,3.9,unreviewed +ATCGAAAAAAAAAAAA-1|AGTGAAAAAAAAAAAA-1,paracrine,low,3.96,unreviewed +ATCGAAAAAAAAAAAA-1|ATCGAAAAAAAAAAAA-1,autocrine,mid,63.64,unreviewed +ATCGAAAAAAAAAAAA-1|CAAGAAAAAAAAAAAA-1,paracrine,high,76.16,unreviewed +ATCGAAAAAAAAAAAA-1|CGTGAAAAAAAAAAAA-1,paracrine,high,91.72,unreviewed +ATCGAAAAAAAAAAAA-1|CTCGAAAAAAAAAAAA-1,paracrine,high,85.14,reviewed +ATCGAAAAAAAAAAAA-1|TGCGAAAAAAAAAAAA-1,paracrine,low,3.35,reviewed +ATCGCAAAAAAAAAAA-1|AAAGCAAAAAAAAAAA-1,paracrine,low,8.28,reviewed +ATCGCAAAAAAAAAAA-1|AGTGCAAAAAAAAAAA-1,paracrine,low,6.21,reviewed +ATCGCAAAAAAAAAAA-1|ATCGCAAAAAAAAAAA-1,autocrine,low,15.9,reviewed +ATCGCAAAAAAAAAAA-1|CTCGCAAAAAAAAAAA-1,paracrine,low,12.85,reviewed +ATCGCAAAAAAAAAAA-1|TCTGCAAAAAAAAAAA-1,paracrine,low,13.4,reviewed +ATCGCAAAAAAAAAAA-1|TGCGCAAAAAAAAAAA-1,paracrine,low,10.9,unreviewed +ATCGCAAAAAAAAAAA-1|TTTCCAAAAAAAAAAA-1,paracrine,low,6.46,reviewed +ATGACAAAAAAAAAAA-1|AACACAAAAAAAAAAA-1,paracrine,mid,62.91,reviewed +ATGACAAAAAAAAAAA-1|AGACCAAAAAAAAAAA-1,paracrine,mid,49.97,reviewed +ATGACAAAAAAAAAAA-1|ATGACAAAAAAAAAAA-1,autocrine,mid,51.28,unreviewed +ATGACAAAAAAAAAAA-1|CTGACAAAAAAAAAAA-1,paracrine,high,72.23,unreviewed +ATGACAAAAAAAAAAA-1|TCACCAAAAAAAAAAA-1,paracrine,mid,50.76,unreviewed +ATGACAAAAAAAAAAA-1|TGGACAAAAAAAAAAA-1,paracrine,low,13.82,unreviewed +ATGACAAAAAAAAAAA-1|TTAACAAAAAAAAAAA-1,paracrine,low,23.84,unreviewed +ATGGCAAAAAAAAAAA-1|AACGCAAAAAAAAAAA-1,paracrine,mid,34.81,reviewed +ATGGCAAAAAAAAAAA-1|AGATCAAAAAAAAAAA-1,paracrine,mid,49.18,unreviewed +ATGGCAAAAAAAAAAA-1|ATGGCAAAAAAAAAAA-1,autocrine,mid,40.56,reviewed +ATGGCAAAAAAAAAAA-1|CACGCAAAAAAAAAAA-1,paracrine,mid,66.08,unreviewed +ATGGCAAAAAAAAAAA-1|CGATCAAAAAAAAAAA-1,paracrine,mid,65.19,reviewed +ATGGCAAAAAAAAAAA-1|CTGGCAAAAAAAAAAA-1,paracrine,high,78.93,unreviewed +ATGGCAAAAAAAAAAA-1|TGGGCAAAAAAAAAAA-1,paracrine,mid,60.57,reviewed +ATGTAAAAAAAAAAAA-1|AACTAAAAAAAAAAAA-1,paracrine,mid,50.58,reviewed +ATGTAAAAAAAAAAAA-1|AGAACAAAAAAAAAAA-1,paracrine,high,81.24,unreviewed +ATGTAAAAAAAAAAAA-1|ATGTAAAAAAAAAAAA-1,autocrine,high,93.57,unreviewed +ATGTAAAAAAAAAAAA-1|CTGTAAAAAAAAAAAA-1,paracrine,high,87.94,unreviewed +ATGTAAAAAAAAAAAA-1|TCAACAAAAAAAAAAA-1,paracrine,high,94.85,reviewed +ATGTAAAAAAAAAAAA-1|TGGTAAAAAAAAAAAA-1,paracrine,mid,58.13,reviewed +ATGTAAAAAAAAAAAA-1|TTATAAAAAAAAAAAA-1,paracrine,mid,48.87,reviewed +ATGTCAAAAAAAAAAA-1|AACTCAAAAAAAAAAA-1,paracrine,low,4.26,unreviewed +ATGTCAAAAAAAAAAA-1|AGAAGAAAAAAAAAAA-1,paracrine,low,3.81,reviewed +ATGTCAAAAAAAAAAA-1|ATGTCAAAAAAAAAAA-1,autocrine,mid,45.01,unreviewed +ATGTCAAAAAAAAAAA-1|CACTCAAAAAAAAAAA-1,paracrine,mid,44.76,unreviewed +ATGTCAAAAAAAAAAA-1|CTGTCAAAAAAAAAAA-1,paracrine,mid,57.8,unreviewed +ATGTCAAAAAAAAAAA-1|TGGTCAAAAAAAAAAA-1,paracrine,low,1.52,unreviewed +ATTACAAAAAAAAAAA-1|AAGACAAAAAAAAAAA-1,paracrine,low,19.79,unreviewed +ATTACAAAAAAAAAAA-1|AGCCCAAAAAAAAAAA-1,paracrine,mid,40.86,unreviewed +ATTACAAAAAAAAAAA-1|ATTACAAAAAAAAAAA-1,autocrine,low,18.91,unreviewed +ATTACAAAAAAAAAAA-1|CAGACAAAAAAAAAAA-1,paracrine,mid,52.62,reviewed +ATTACAAAAAAAAAAA-1|CGCCCAAAAAAAAAAA-1,paracrine,mid,62.3,unreviewed +ATTACAAAAAAAAAAA-1|CTTACAAAAAAAAAAA-1,paracrine,low,17.36,unreviewed +ATTACAAAAAAAAAAA-1|TGTACAAAAAAAAAAA-1,paracrine,low,13.76,unreviewed +ATTCAAAAAAAAAAAA-1|AAGCAAAAAAAAAAAA-1,paracrine,mid,40.8,unreviewed +ATTCAAAAAAAAAAAA-1|AGCGAAAAAAAAAAAA-1,paracrine,high,69.46,reviewed +ATTCAAAAAAAAAAAA-1|ATTCAAAAAAAAAAAA-1,autocrine,mid,42.97,reviewed +ATTCAAAAAAAAAAAA-1|CTTCAAAAAAAAAAAA-1,paracrine,mid,39.65,unreviewed +ATTCAAAAAAAAAAAA-1|TCCGAAAAAAAAAAAA-1,paracrine,low,31.52,reviewed +ATTCAAAAAAAAAAAA-1|TGTCAAAAAAAAAAAA-1,paracrine,high,68.03,unreviewed +ATTCAAAAAAAAAAAA-1|TTCCAAAAAAAAAAAA-1,paracrine,low,19.21,unreviewed +ATTCCAAAAAAAAAAA-1|AAGCCAAAAAAAAAAA-1,paracrine,low,3.65,unreviewed +ATTCCAAAAAAAAAAA-1|AGCGCAAAAAAAAAAA-1,paracrine,low,3.71,reviewed +ATTCCAAAAAAAAAAA-1|ATTCCAAAAAAAAAAA-1,autocrine,high,70.16,unreviewed +ATTCCAAAAAAAAAAA-1|CAGCCAAAAAAAAAAA-1,paracrine,low,23.23,reviewed +ATTCCAAAAAAAAAAA-1|CGCGCAAAAAAAAAAA-1,paracrine,mid,66.66,unreviewed +ATTCCAAAAAAAAAAA-1|CTTCCAAAAAAAAAAA-1,paracrine,high,92.45,unreviewed +ATTCCAAAAAAAAAAA-1|TGTCCAAAAAAAAAAA-1,paracrine,low,4.75,reviewed +ATTGAAAAAAAAAAAA-1|AAGGAAAAAAAAAAAA-1,paracrine,low,33.04,reviewed +ATTGAAAAAAAAAAAA-1|AGCTAAAAAAAAAAAA-1,paracrine,mid,35.02,reviewed +ATTGAAAAAAAAAAAA-1|ATTGAAAAAAAAAAAA-1,autocrine,high,73.63,unreviewed +ATTGAAAAAAAAAAAA-1|CTTGAAAAAAAAAAAA-1,paracrine,high,73.11,unreviewed +ATTGAAAAAAAAAAAA-1|TCCTAAAAAAAAAAAA-1,paracrine,mid,53.81,reviewed +ATTGAAAAAAAAAAAA-1|TGTGAAAAAAAAAAAA-1,paracrine,mid,45.34,unreviewed +ATTGAAAAAAAAAAAA-1|TTCGAAAAAAAAAAAA-1,paracrine,high,88.82,unreviewed +CAAACAAAAAAAAAAA-1|AAAACAAAAAAAAAAA-1,paracrine,high,79.54,unreviewed +CAAACAAAAAAAAAAA-1|CAAACAAAAAAAAAAA-1,autocrine,mid,45.83,reviewed +CAAACAAAAAAAAAAA-1|CCGTAAAAAAAAAAAA-1,paracrine,mid,66.02,unreviewed +CAAACAAAAAAAAAAA-1|CTCACAAAAAAAAAAA-1,paracrine,low,27.25,unreviewed +CAAACAAAAAAAAAAA-1|GAAACAAAAAAAAAAA-1,paracrine,low,22.47,unreviewed +CAAACAAAAAAAAAAA-1|GCGTAAAAAAAAAAAA-1,paracrine,low,24.54,unreviewed +CAAACAAAAAAAAAAA-1|GTCACAAAAAAAAAAA-1,paracrine,low,17.24,reviewed +CAACCAAAAAAAAAAA-1|AAACCAAAAAAAAAAA-1,paracrine,high,71.99,unreviewed +CAACCAAAAAAAAAAA-1|CAACCAAAAAAAAAAA-1,autocrine,mid,54.66,reviewed +CAACCAAAAAAAAAAA-1|CCGACAAAAAAAAAAA-1,paracrine,high,69.73,unreviewed +CAACCAAAAAAAAAAA-1|CTCCCAAAAAAAAAAA-1,paracrine,mid,56.67,unreviewed +CAACCAAAAAAAAAAA-1|GAACCAAAAAAAAAAA-1,paracrine,mid,42.33,reviewed +CAACCAAAAAAAAAAA-1|GCGACAAAAAAAAAAA-1,paracrine,mid,40.19,reviewed +CAACCAAAAAAAAAAA-1|GTCCCAAAAAAAAAAA-1,paracrine,high,79.02,unreviewed +CAAGAAAAAAAAAAAA-1|AAAGAAAAAAAAAAAA-1,paracrine,low,4.32,unreviewed +CAAGAAAAAAAAAAAA-1|ACGCAAAAAAAAAAAA-1,paracrine,low,22.02,reviewed +CAAGAAAAAAAAAAAA-1|ATCGAAAAAAAAAAAA-1,paracrine,low,18.0,unreviewed +CAAGAAAAAAAAAAAA-1|CAAGAAAAAAAAAAAA-1,autocrine,low,28.47,unreviewed +CAAGAAAAAAAAAAAA-1|CTCGAAAAAAAAAAAA-1,paracrine,mid,38.25,unreviewed +CAAGAAAAAAAAAAAA-1|GAAGAAAAAAAAAAAA-1,paracrine,low,23.84,unreviewed +CAAGCAAAAAAAAAAA-1|AAAGCAAAAAAAAAAA-1,paracrine,mid,56.67,unreviewed +CAAGCAAAAAAAAAAA-1|CAAGCAAAAAAAAAAA-1,autocrine,high,96.1,reviewed +CAAGCAAAAAAAAAAA-1|CCGCCAAAAAAAAAAA-1,paracrine,high,94.28,unreviewed +CAAGCAAAAAAAAAAA-1|CTCGCAAAAAAAAAAA-1,paracrine,high,80.57,reviewed +CAAGCAAAAAAAAAAA-1|GAAGCAAAAAAAAAAA-1,paracrine,mid,61.54,reviewed +CAAGCAAAAAAAAAAA-1|GCGCCAAAAAAAAAAA-1,paracrine,high,88.31,unreviewed +CAAGCAAAAAAAAAAA-1|GTCGCAAAAAAAAAAA-1,paracrine,high,96.16,unreviewed +CACACAAAAAAAAAAA-1|AACACAAAAAAAAAAA-1,paracrine,high,69.61,reviewed +CACACAAAAAAAAAAA-1|CACACAAAAAAAAAAA-1,autocrine,high,90.65,unreviewed +CACACAAAAAAAAAAA-1|CCTTAAAAAAAAAAAA-1,paracrine,high,84.32,reviewed +CACACAAAAAAAAAAA-1|CTGACAAAAAAAAAAA-1,paracrine,high,82.19,unreviewed +CACACAAAAAAAAAAA-1|GACACAAAAAAAAAAA-1,paracrine,mid,62.52,unreviewed +CACACAAAAAAAAAAA-1|GCTTAAAAAAAAAAAA-1,paracrine,high,79.45,unreviewed +CACACAAAAAAAAAAA-1|GTGACAAAAAAAAAAA-1,paracrine,mid,49.18,reviewed +CACGCAAAAAAAAAAA-1|AACGCAAAAAAAAAAA-1,paracrine,low,17.51,unreviewed +CACGCAAAAAAAAAAA-1|ACTCCAAAAAAAAAAA-1,paracrine,low,33.25,reviewed +CACGCAAAAAAAAAAA-1|ATGGCAAAAAAAAAAA-1,paracrine,low,15.68,unreviewed +CACGCAAAAAAAAAAA-1|CACGCAAAAAAAAAAA-1,autocrine,mid,46.13,reviewed +CACGCAAAAAAAAAAA-1|CCTCCAAAAAAAAAAA-1,paracrine,low,23.93,unreviewed +CACGCAAAAAAAAAAA-1|CTGGCAAAAAAAAAAA-1,paracrine,mid,50.12,unreviewed +CACGCAAAAAAAAAAA-1|GACGCAAAAAAAAAAA-1,paracrine,low,24.3,unreviewed +CACTAAAAAAAAAAAA-1|AACTAAAAAAAAAAAA-1,paracrine,low,18.0,unreviewed +CACTAAAAAAAAAAAA-1|CACTAAAAAAAAAAAA-1,autocrine,low,11.18,unreviewed +CACTAAAAAAAAAAAA-1|CCTGAAAAAAAAAAAA-1,paracrine,mid,59.81,unreviewed +CACTAAAAAAAAAAAA-1|CTGTAAAAAAAAAAAA-1,paracrine,mid,51.28,unreviewed +CACTAAAAAAAAAAAA-1|GACTAAAAAAAAAAAA-1,paracrine,mid,60.32,unreviewed +CACTAAAAAAAAAAAA-1|GCTGAAAAAAAAAAAA-1,paracrine,mid,60.32,reviewed +CACTAAAAAAAAAAAA-1|GTGTAAAAAAAAAAAA-1,paracrine,low,26.31,reviewed +CACTCAAAAAAAAAAA-1|AACTCAAAAAAAAAAA-1,paracrine,low,4.51,unreviewed +CACTCAAAAAAAAAAA-1|ACTGCAAAAAAAAAAA-1,paracrine,low,17.51,unreviewed +CACTCAAAAAAAAAAA-1|ATGTCAAAAAAAAAAA-1,paracrine,low,20.37,reviewed +CACTCAAAAAAAAAAA-1|CACTCAAAAAAAAAAA-1,autocrine,low,28.84,unreviewed +CACTCAAAAAAAAAAA-1|CCTGCAAAAAAAAAAA-1,paracrine,low,28.75,unreviewed +CACTCAAAAAAAAAAA-1|CTGTCAAAAAAAAAAA-1,paracrine,mid,44.21,unreviewed +CACTCAAAAAAAAAAA-1|GACTCAAAAAAAAAAA-1,paracrine,mid,65.19,unreviewed +CAGACAAAAAAAAAAA-1|AAGACAAAAAAAAAAA-1,paracrine,mid,53.41,reviewed +CAGACAAAAAAAAAAA-1|ACAACAAAAAAAAAAA-1,paracrine,high,84.14,unreviewed +CAGACAAAAAAAAAAA-1|ATTACAAAAAAAAAAA-1,paracrine,mid,47.72,unreviewed +CAGACAAAAAAAAAAA-1|CAGACAAAAAAAAAAA-1,autocrine,high,87.27,unreviewed +CAGACAAAAAAAAAAA-1|CCAACAAAAAAAAAAA-1,paracrine,mid,56.03,unreviewed +CAGACAAAAAAAAAAA-1|CTTACAAAAAAAAAAA-1,paracrine,mid,40.38,unreviewed +CAGACAAAAAAAAAAA-1|GAGACAAAAAAAAAAA-1,paracrine,mid,35.38,reviewed +CAGCAAAAAAAAAAAA-1|AAGCAAAAAAAAAAAA-1,paracrine,mid,52.13,unreviewed +CAGCAAAAAAAAAAAA-1|CAGCAAAAAAAAAAAA-1,autocrine,low,32.8,unreviewed +CAGCAAAAAAAAAAAA-1|CTTCAAAAAAAAAAAA-1,paracrine,mid,49.97,reviewed +CAGCAAAAAAAAAAAA-1|GAGCAAAAAAAAAAAA-1,paracrine,mid,41.9,unreviewed +CAGCAAAAAAAAAAAA-1|GTTCAAAAAAAAAAAA-1,paracrine,low,31.76,reviewed +CAGCCAAAAAAAAAAA-1|AAGCCAAAAAAAAAAA-1,paracrine,low,3.17,unreviewed +CAGCCAAAAAAAAAAA-1|ACACCAAAAAAAAAAA-1,paracrine,low,12.79,unreviewed +CAGCCAAAAAAAAAAA-1|ATTCCAAAAAAAAAAA-1,paracrine,low,24.94,reviewed +CAGCCAAAAAAAAAAA-1|CAGCCAAAAAAAAAAA-1,autocrine,low,9.14,reviewed +CAGCCAAAAAAAAAAA-1|CCACCAAAAAAAAAAA-1,paracrine,low,31.61,unreviewed +CAGCCAAAAAAAAAAA-1|CTTCCAAAAAAAAAAA-1,paracrine,mid,51.28,unreviewed +CAGCCAAAAAAAAAAA-1|GAGCCAAAAAAAAAAA-1,paracrine,low,31.36,reviewed +CATCCAAAAAAAAAAA-1|AATCCAAAAAAAAAAA-1,paracrine,low,21.07,reviewed +CATCCAAAAAAAAAAA-1|CATCCAAAAAAAAAAA-1,autocrine,low,29.35,unreviewed +CATCCAAAAAAAAAAA-1|CCCCCAAAAAAAAAAA-1,paracrine,low,22.84,reviewed +CATCCAAAAAAAAAAA-1|CTAGCAAAAAAAAAAA-1,paracrine,mid,38.67,unreviewed +CATCCAAAAAAAAAAA-1|GATCCAAAAAAAAAAA-1,paracrine,mid,33.4,unreviewed +CATCCAAAAAAAAAAA-1|GCCCCAAAAAAAAAAA-1,paracrine,low,24.7,unreviewed +CATCCAAAAAAAAAAA-1|GTAGCAAAAAAAAAAA-1,paracrine,mid,44.88,unreviewed +CATGAAAAAAAAAAAA-1|AATGAAAAAAAAAAAA-1,paracrine,low,12.48,unreviewed +CATGAAAAAAAAAAAA-1|ACCGAAAAAAAAAAAA-1,paracrine,low,14.25,unreviewed +CATGAAAAAAAAAAAA-1|ATATAAAAAAAAAAAA-1,paracrine,low,22.29,unreviewed +CATGAAAAAAAAAAAA-1|CATGAAAAAAAAAAAA-1,autocrine,low,6.64,unreviewed +CATGAAAAAAAAAAAA-1|CCCGAAAAAAAAAAAA-1,paracrine,mid,37.97,reviewed +CATGAAAAAAAAAAAA-1|CTATAAAAAAAAAAAA-1,paracrine,low,19.06,unreviewed +CATGAAAAAAAAAAAA-1|GATGAAAAAAAAAAAA-1,paracrine,low,7.49,unreviewed +CATGCAAAAAAAAAAA-1|AATGCAAAAAAAAAAA-1,paracrine,high,82.64,unreviewed +CATGCAAAAAAAAAAA-1|CATGCAAAAAAAAAAA-1,autocrine,high,89.13,unreviewed +CATGCAAAAAAAAAAA-1|CCCGCAAAAAAAAAAA-1,paracrine,low,24.85,reviewed +CATGCAAAAAAAAAAA-1|CTATCAAAAAAAAAAA-1,paracrine,high,83.13,reviewed +CATGCAAAAAAAAAAA-1|GATGCAAAAAAAAAAA-1,paracrine,high,93.36,reviewed +CATGCAAAAAAAAAAA-1|GCCGCAAAAAAAAAAA-1,paracrine,high,82.58,reviewed +CATGCAAAAAAAAAAA-1|GTATCAAAAAAAAAAA-1,paracrine,high,77.1,unreviewed +CATTAAAAAAAAAAAA-1|AATTAAAAAAAAAAAA-1,paracrine,low,4.87,unreviewed +CATTAAAAAAAAAAAA-1|ACCTAAAAAAAAAAAA-1,paracrine,high,86.27,reviewed +CATTAAAAAAAAAAAA-1|ATAACAAAAAAAAAAA-1,paracrine,high,75.06,unreviewed +CATTAAAAAAAAAAAA-1|CATTAAAAAAAAAAAA-1,autocrine,mid,63.46,unreviewed +CATTAAAAAAAAAAAA-1|CCCTAAAAAAAAAAAA-1,paracrine,high,87.48,reviewed +CATTAAAAAAAAAAAA-1|CTAACAAAAAAAAAAA-1,paracrine,low,18.57,reviewed +CATTAAAAAAAAAAAA-1|GATTAAAAAAAAAAAA-1,paracrine,high,78.62,unreviewed +CCAACAAAAAAAAAAA-1|ACAACAAAAAAAAAAA-1,paracrine,high,87.03,unreviewed +CCAACAAAAAAAAAAA-1|CAGACAAAAAAAAAAA-1,paracrine,high,89.34,reviewed +CCAACAAAAAAAAAAA-1|CCAACAAAAAAAAAAA-1,autocrine,mid,60.41,unreviewed +CCAACAAAAAAAAAAA-1|CGGTAAAAAAAAAAAA-1,paracrine,high,79.87,unreviewed +CCAACAAAAAAAAAAA-1|GAGACAAAAAAAAAAA-1,paracrine,mid,33.71,unreviewed +CCAACAAAAAAAAAAA-1|GCAACAAAAAAAAAAA-1,paracrine,high,82.76,unreviewed +CCAACAAAAAAAAAAA-1|GGGTAAAAAAAAAAAA-1,paracrine,mid,61.63,unreviewed +CCAAGAAAAAAAAAAA-1|ACAAGAAAAAAAAAAA-1,paracrine,low,13.12,unreviewed +CCAAGAAAAAAAAAAA-1|AGGTCAAAAAAAAAAA-1,paracrine,mid,47.87,reviewed +CCAAGAAAAAAAAAAA-1|CCAAGAAAAAAAAAAA-1,autocrine,mid,42.2,unreviewed +CCAAGAAAAAAAAAAA-1|CGGTCAAAAAAAAAAA-1,paracrine,high,78.71,unreviewed +CCAAGAAAAAAAAAAA-1|GCAAGAAAAAAAAAAA-1,paracrine,low,32.67,reviewed +CCACCAAAAAAAAAAA-1|ACACCAAAAAAAAAAA-1,paracrine,low,30.36,unreviewed +CCACCAAAAAAAAAAA-1|CAGCCAAAAAAAAAAA-1,paracrine,low,14.49,unreviewed +CCACCAAAAAAAAAAA-1|CCACCAAAAAAAAAAA-1,autocrine,mid,59.1,unreviewed +CCACCAAAAAAAAAAA-1|CGGACAAAAAAAAAAA-1,paracrine,mid,46.89,reviewed +CCACCAAAAAAAAAAA-1|GAGCCAAAAAAAAAAA-1,paracrine,mid,53.62,unreviewed +CCACCAAAAAAAAAAA-1|GCACCAAAAAAAAAAA-1,paracrine,mid,52.13,unreviewed +CCACCAAAAAAAAAAA-1|GGGACAAAAAAAAAAA-1,paracrine,high,67.6,unreviewed +CCCCCAAAAAAAAAAA-1|AATCCAAAAAAAAAAA-1,paracrine,high,81.06,unreviewed +CCCCCAAAAAAAAAAA-1|ACCCCAAAAAAAAAAA-1,paracrine,high,68.97,reviewed +CCCCCAAAAAAAAAAA-1|AGTACAAAAAAAAAAA-1,paracrine,mid,43.12,unreviewed +CCCCCAAAAAAAAAAA-1|CATCCAAAAAAAAAAA-1,paracrine,high,88.43,unreviewed +CCCCCAAAAAAAAAAA-1|CCCCCAAAAAAAAAAA-1,autocrine,high,83.89,unreviewed +CCCCCAAAAAAAAAAA-1|CGTACAAAAAAAAAAA-1,paracrine,high,75.58,unreviewed +CCCCCAAAAAAAAAAA-1|GCCCCAAAAAAAAAAA-1,paracrine,high,80.97,reviewed +CCCGAAAAAAAAAAAA-1|ACCGAAAAAAAAAAAA-1,paracrine,mid,43.18,reviewed +CCCGAAAAAAAAAAAA-1|CATGAAAAAAAAAAAA-1,paracrine,low,11.42,unreviewed +CCCGAAAAAAAAAAAA-1|CCCGAAAAAAAAAAAA-1,autocrine,high,76.67,unreviewed +CCCGAAAAAAAAAAAA-1|CGTCAAAAAAAAAAAA-1,paracrine,mid,57.7,unreviewed +CCCGAAAAAAAAAAAA-1|GATGAAAAAAAAAAAA-1,paracrine,low,15.5,unreviewed +CCCGAAAAAAAAAAAA-1|GCCGAAAAAAAAAAAA-1,paracrine,mid,48.87,reviewed +CCCGAAAAAAAAAAAA-1|GGTCAAAAAAAAAAAA-1,paracrine,mid,64.86,unreviewed +CCCGCAAAAAAAAAAA-1|AATGCAAAAAAAAAAA-1,paracrine,mid,53.62,reviewed +CCCGCAAAAAAAAAAA-1|ACCGCAAAAAAAAAAA-1,paracrine,low,30.72,reviewed +CCCGCAAAAAAAAAAA-1|AGTCCAAAAAAAAAAA-1,paracrine,mid,49.48,reviewed +CCCGCAAAAAAAAAAA-1|CATGCAAAAAAAAAAA-1,paracrine,mid,65.56,unreviewed +CCCGCAAAAAAAAAAA-1|CCCGCAAAAAAAAAAA-1,autocrine,low,16.02,unreviewed +CCCGCAAAAAAAAAAA-1|CGTCCAAAAAAAAAAA-1,paracrine,high,69.06,unreviewed +CCCGCAAAAAAAAAAA-1|GCCGCAAAAAAAAAAA-1,paracrine,mid,58.34,unreviewed +CCCTAAAAAAAAAAAA-1|ACCTAAAAAAAAAAAA-1,paracrine,high,86.57,unreviewed +CCCTAAAAAAAAAAAA-1|CATTAAAAAAAAAAAA-1,paracrine,mid,63.09,unreviewed +CCCTAAAAAAAAAAAA-1|CCCTAAAAAAAAAAAA-1,autocrine,high,89.04,unreviewed +CCCTAAAAAAAAAAAA-1|CGTGAAAAAAAAAAAA-1,paracrine,high,87.48,reviewed +CCCTAAAAAAAAAAAA-1|GATTAAAAAAAAAAAA-1,paracrine,high,74.48,unreviewed +CCCTAAAAAAAAAAAA-1|GCCTAAAAAAAAAAAA-1,paracrine,high,89.8,unreviewed +CCCTAAAAAAAAAAAA-1|GGTGAAAAAAAAAAAA-1,paracrine,high,77.04,unreviewed +CCGACAAAAAAAAAAA-1|AAACCAAAAAAAAAAA-1,paracrine,mid,52.13,unreviewed +CCGACAAAAAAAAAAA-1|ACGACAAAAAAAAAAA-1,paracrine,mid,33.86,reviewed +CCGACAAAAAAAAAAA-1|AGAACAAAAAAAAAAA-1,paracrine,mid,51.89,unreviewed +CCGACAAAAAAAAAAA-1|CAACCAAAAAAAAAAA-1,paracrine,mid,53.14,unreviewed +CCGACAAAAAAAAAAA-1|CCGACAAAAAAAAAAA-1,autocrine,mid,48.75,reviewed +CCGACAAAAAAAAAAA-1|CGAACAAAAAAAAAAA-1,paracrine,low,32.37,reviewed +CCGACAAAAAAAAAAA-1|GCGACAAAAAAAAAAA-1,paracrine,low,26.16,unreviewed +CCGCCAAAAAAAAAAA-1|AAAGCAAAAAAAAAAA-1,paracrine,high,80.27,unreviewed +CCGCCAAAAAAAAAAA-1|ACGCCAAAAAAAAAAA-1,paracrine,mid,59.38,unreviewed +CCGCCAAAAAAAAAAA-1|AGACCAAAAAAAAAAA-1,paracrine,high,84.77,reviewed +CCGCCAAAAAAAAAAA-1|CAAGCAAAAAAAAAAA-1,paracrine,high,99.76,unreviewed +CCGCCAAAAAAAAAAA-1|CCGCCAAAAAAAAAAA-1,autocrine,high,97.81,unreviewed +CCGCCAAAAAAAAAAA-1|CGACCAAAAAAAAAAA-1,paracrine,high,75.52,unreviewed +CCGCCAAAAAAAAAAA-1|GCGCCAAAAAAAAAAA-1,paracrine,high,96.5,unreviewed +CCGTAAAAAAAAAAAA-1|AAAACAAAAAAAAAAA-1,paracrine,high,94.95,unreviewed +CCGTAAAAAAAAAAAA-1|ACGTAAAAAAAAAAAA-1,paracrine,mid,40.71,unreviewed +CCGTAAAAAAAAAAAA-1|CAAACAAAAAAAAAAA-1,paracrine,high,84.87,reviewed +CCGTAAAAAAAAAAAA-1|CCGTAAAAAAAAAAAA-1,autocrine,high,86.48,reviewed +CCGTAAAAAAAAAAAA-1|CGATAAAAAAAAAAAA-1,paracrine,high,79.23,unreviewed +CCGTAAAAAAAAAAAA-1|GCGTAAAAAAAAAAAA-1,paracrine,mid,50.94,unreviewed +CCGTCAAAAAAAAAAA-1|ACGTCAAAAAAAAAAA-1,paracrine,high,74.6,unreviewed +CCGTCAAAAAAAAAAA-1|CCGTCAAAAAAAAAAA-1,autocrine,mid,62.09,unreviewed +CCGTCAAAAAAAAAAA-1|CGATCAAAAAAAAAAA-1,paracrine,high,79.78,reviewed +CCGTCAAAAAAAAAAA-1|GCGTCAAAAAAAAAAA-1,paracrine,high,83.8,unreviewed +CCGTCAAAAAAAAAAA-1|GGATCAAAAAAAAAAA-1,paracrine,high,81.18,reviewed +CCTCCAAAAAAAAAAA-1|ACTCCAAAAAAAAAAA-1,paracrine,high,72.32,unreviewed +CCTCCAAAAAAAAAAA-1|CACGCAAAAAAAAAAA-1,paracrine,high,79.69,reviewed +CCTCCAAAAAAAAAAA-1|CCTCCAAAAAAAAAAA-1,autocrine,mid,66.2,reviewed +CCTCCAAAAAAAAAAA-1|CGCCCAAAAAAAAAAA-1,paracrine,high,84.96,reviewed +CCTCCAAAAAAAAAAA-1|GACGCAAAAAAAAAAA-1,paracrine,mid,59.68,reviewed +CCTCCAAAAAAAAAAA-1|GCTCCAAAAAAAAAAA-1,paracrine,high,74.76,unreviewed +CCTCCAAAAAAAAAAA-1|GGCCCAAAAAAAAAAA-1,paracrine,high,70.37,unreviewed +CCTGAAAAAAAAAAAA-1|AACTAAAAAAAAAAAA-1,paracrine,mid,40.56,unreviewed +CCTGAAAAAAAAAAAA-1|ACTGAAAAAAAAAAAA-1,paracrine,high,79.6,unreviewed +CCTGAAAAAAAAAAAA-1|AGCGAAAAAAAAAAAA-1,paracrine,high,89.98,reviewed +CCTGAAAAAAAAAAAA-1|CACTAAAAAAAAAAAA-1,paracrine,low,31.52,unreviewed +CCTGAAAAAAAAAAAA-1|CCTGAAAAAAAAAAAA-1,autocrine,high,88.82,reviewed +CCTGAAAAAAAAAAAA-1|CGCGAAAAAAAAAAAA-1,paracrine,high,94.4,reviewed +CCTGAAAAAAAAAAAA-1|GCTGAAAAAAAAAAAA-1,paracrine,high,91.26,unreviewed +CCTGCAAAAAAAAAAA-1|ACTGCAAAAAAAAAAA-1,paracrine,low,15.1,unreviewed +CCTGCAAAAAAAAAAA-1|CACTCAAAAAAAAAAA-1,paracrine,low,27.89,unreviewed +CCTGCAAAAAAAAAAA-1|CCTGCAAAAAAAAAAA-1,autocrine,low,21.59,reviewed +CCTGCAAAAAAAAAAA-1|CGCGCAAAAAAAAAAA-1,paracrine,low,28.84,unreviewed +CCTGCAAAAAAAAAAA-1|GACTCAAAAAAAAAAA-1,paracrine,mid,47.72,unreviewed +CCTGCAAAAAAAAAAA-1|GCTGCAAAAAAAAAAA-1,paracrine,mid,60.05,unreviewed +CCTGCAAAAAAAAAAA-1|GGCGCAAAAAAAAAAA-1,paracrine,low,15.38,reviewed +CCTTAAAAAAAAAAAA-1|AACACAAAAAAAAAAA-1,paracrine,high,70.46,reviewed +CCTTAAAAAAAAAAAA-1|ACTTAAAAAAAAAAAA-1,paracrine,mid,37.85,reviewed +CCTTAAAAAAAAAAAA-1|AGCTAAAAAAAAAAAA-1,paracrine,low,27.07,reviewed +CCTTAAAAAAAAAAAA-1|CACACAAAAAAAAAAA-1,paracrine,high,92.2,reviewed +CCTTAAAAAAAAAAAA-1|CCTTAAAAAAAAAAAA-1,autocrine,high,90.99,reviewed +CCTTAAAAAAAAAAAA-1|CGCTAAAAAAAAAAAA-1,paracrine,high,66.81,reviewed +CCTTAAAAAAAAAAAA-1|GCTTAAAAAAAAAAAA-1,paracrine,high,89.28,unreviewed +CGAACAAAAAAAAAAA-1|AGAACAAAAAAAAAAA-1,paracrine,low,27.25,unreviewed +CGAACAAAAAAAAAAA-1|CCGACAAAAAAAAAAA-1,paracrine,low,18.0,reviewed +CGAACAAAAAAAAAAA-1|CGAACAAAAAAAAAAA-1,autocrine,low,21.59,reviewed +CGAACAAAAAAAAAAA-1|CTGTAAAAAAAAAAAA-1,paracrine,low,25.82,unreviewed +CGAACAAAAAAAAAAA-1|GCGACAAAAAAAAAAA-1,paracrine,low,16.38,unreviewed +CGAACAAAAAAAAAAA-1|GGAACAAAAAAAAAAA-1,paracrine,low,14.31,reviewed +CGAACAAAAAAAAAAA-1|GTGTAAAAAAAAAAAA-1,paracrine,low,10.35,unreviewed +CGACCAAAAAAAAAAA-1|AGACCAAAAAAAAAAA-1,paracrine,mid,38.95,unreviewed +CGACCAAAAAAAAAAA-1|CCGCCAAAAAAAAAAA-1,paracrine,high,68.3,unreviewed +CGACCAAAAAAAAAAA-1|CGACCAAAAAAAAAAA-1,autocrine,low,27.41,reviewed +CGACCAAAAAAAAAAA-1|CTGACAAAAAAAAAAA-1,paracrine,mid,66.41,reviewed +CGACCAAAAAAAAAAA-1|GCGCCAAAAAAAAAAA-1,paracrine,mid,57.16,unreviewed +CGACCAAAAAAAAAAA-1|GGACCAAAAAAAAAAA-1,paracrine,high,79.17,reviewed +CGACCAAAAAAAAAAA-1|GTGACAAAAAAAAAAA-1,paracrine,low,26.0,unreviewed +CGATAAAAAAAAAAAA-1|CCGTAAAAAAAAAAAA-1,paracrine,high,70.52,unreviewed +CGATAAAAAAAAAAAA-1|CGATAAAAAAAAAAAA-1,autocrine,mid,57.31,reviewed +CGATAAAAAAAAAAAA-1|GCGTAAAAAAAAAAAA-1,paracrine,mid,33.56,reviewed +CGATAAAAAAAAAAAA-1|GGATAAAAAAAAAAAA-1,paracrine,mid,39.28,unreviewed +CGATAAAAAAAAAAAA-1|GTGGAAAAAAAAAAAA-1,paracrine,mid,38.49,reviewed +CGATCAAAAAAAAAAA-1|ACGTCAAAAAAAAAAA-1,paracrine,low,32.89,reviewed +CGATCAAAAAAAAAAA-1|AGATCAAAAAAAAAAA-1,paracrine,low,29.08,reviewed +CGATCAAAAAAAAAAA-1|ATGGCAAAAAAAAAAA-1,paracrine,low,23.54,reviewed +CGATCAAAAAAAAAAA-1|CCGTCAAAAAAAAAAA-1,paracrine,low,22.23,unreviewed +CGATCAAAAAAAAAAA-1|CGATCAAAAAAAAAAA-1,autocrine,mid,44.55,reviewed +CGATCAAAAAAAAAAA-1|CTGGCAAAAAAAAAAA-1,paracrine,mid,57.0,unreviewed +CGATCAAAAAAAAAAA-1|GGATCAAAAAAAAAAA-1,paracrine,mid,42.75,unreviewed +CGCCCAAAAAAAAAAA-1|ACTCCAAAAAAAAAAA-1,paracrine,high,74.15,unreviewed +CGCCCAAAAAAAAAAA-1|AGCCCAAAAAAAAAAA-1,paracrine,high,75.73,unreviewed +CGCCCAAAAAAAAAAA-1|ATTACAAAAAAAAAAA-1,paracrine,mid,46.29,unreviewed +CGCCCAAAAAAAAAAA-1|CCTCCAAAAAAAAAAA-1,paracrine,mid,64.98,unreviewed +CGCCCAAAAAAAAAAA-1|CGCCCAAAAAAAAAAA-1,autocrine,high,91.53,reviewed +CGCCCAAAAAAAAAAA-1|CTTACAAAAAAAAAAA-1,paracrine,mid,39.86,reviewed +CGCCCAAAAAAAAAAA-1|GGCCCAAAAAAAAAAA-1,paracrine,high,75.15,unreviewed +CGCGAAAAAAAAAAAA-1|AGCGAAAAAAAAAAAA-1,paracrine,high,86.08,unreviewed +CGCGAAAAAAAAAAAA-1|CCTGAAAAAAAAAAAA-1,paracrine,high,82.1,reviewed +CGCGAAAAAAAAAAAA-1|CGCGAAAAAAAAAAAA-1,autocrine,high,89.68,reviewed +CGCGAAAAAAAAAAAA-1|CTTCAAAAAAAAAAAA-1,paracrine,mid,61.33,unreviewed +CGCGAAAAAAAAAAAA-1|GCTGAAAAAAAAAAAA-1,paracrine,high,83.62,reviewed +CGCGAAAAAAAAAAAA-1|GGCGAAAAAAAAAAAA-1,paracrine,low,27.34,unreviewed +CGCGAAAAAAAAAAAA-1|GTTCAAAAAAAAAAAA-1,paracrine,mid,38.73,unreviewed +CGCGCAAAAAAAAAAA-1|ACTGCAAAAAAAAAAA-1,paracrine,low,28.99,unreviewed +CGCGCAAAAAAAAAAA-1|AGCGCAAAAAAAAAAA-1,paracrine,low,3.81,reviewed +CGCGCAAAAAAAAAAA-1|ATTCCAAAAAAAAAAA-1,paracrine,mid,62.36,reviewed +CGCGCAAAAAAAAAAA-1|CCTGCAAAAAAAAAAA-1,paracrine,mid,46.99,unreviewed +CGCGCAAAAAAAAAAA-1|CGCGCAAAAAAAAAAA-1,autocrine,mid,56.52,unreviewed +CGCGCAAAAAAAAAAA-1|CTTCCAAAAAAAAAAA-1,paracrine,high,86.75,unreviewed +CGCGCAAAAAAAAAAA-1|GGCGCAAAAAAAAAAA-1,paracrine,mid,36.72,unreviewed +CGCTAAAAAAAAAAAA-1|AGCTAAAAAAAAAAAA-1,paracrine,low,19.06,unreviewed +CGCTAAAAAAAAAAAA-1|CCTTAAAAAAAAAAAA-1,paracrine,high,71.04,unreviewed +CGCTAAAAAAAAAAAA-1|CGCTAAAAAAAAAAAA-1,autocrine,mid,46.44,reviewed +CGCTAAAAAAAAAAAA-1|CTTGAAAAAAAAAAAA-1,paracrine,mid,57.0,unreviewed +CGCTAAAAAAAAAAAA-1|GCTTAAAAAAAAAAAA-1,paracrine,high,69.95,reviewed +CGCTAAAAAAAAAAAA-1|GGCTAAAAAAAAAAAA-1,paracrine,low,30.6,unreviewed +CGGACAAAAAAAAAAA-1|ACACCAAAAAAAAAAA-1,paracrine,low,16.99,unreviewed +CGGACAAAAAAAAAAA-1|AGGACAAAAAAAAAAA-1,paracrine,low,5.12,unreviewed +CGGACAAAAAAAAAAA-1|ATAACAAAAAAAAAAA-1,paracrine,low,29.45,unreviewed +CGGACAAAAAAAAAAA-1|CCACCAAAAAAAAAAA-1,paracrine,mid,35.2,reviewed +CGGACAAAAAAAAAAA-1|CGGACAAAAAAAAAAA-1,autocrine,low,31.36,reviewed +CGGACAAAAAAAAAAA-1|CTAACAAAAAAAAAAA-1,paracrine,low,6.94,unreviewed +CGGACAAAAAAAAAAA-1|GGGACAAAAAAAAAAA-1,paracrine,mid,39.86,unreviewed +CGGTAAAAAAAAAAAA-1|ACAACAAAAAAAAAAA-1,paracrine,high,76.89,reviewed +CGGTAAAAAAAAAAAA-1|AGGTAAAAAAAAAAAA-1,paracrine,mid,55.09,unreviewed +CGGTAAAAAAAAAAAA-1|ATATAAAAAAAAAAAA-1,paracrine,high,73.54,unreviewed +CGGTAAAAAAAAAAAA-1|CCAACAAAAAAAAAAA-1,paracrine,mid,49.57,unreviewed +CGGTAAAAAAAAAAAA-1|CGGTAAAAAAAAAAAA-1,autocrine,high,69.24,unreviewed +CGGTAAAAAAAAAAAA-1|CTATAAAAAAAAAAAA-1,paracrine,high,66.87,unreviewed +CGGTAAAAAAAAAAAA-1|GGGTAAAAAAAAAAAA-1,paracrine,mid,52.77,unreviewed +CGGTCAAAAAAAAAAA-1|AGGTCAAAAAAAAAAA-1,paracrine,mid,55.85,reviewed +CGGTCAAAAAAAAAAA-1|CCAAGAAAAAAAAAAA-1,paracrine,mid,46.77,unreviewed +CGGTCAAAAAAAAAAA-1|CGGTCAAAAAAAAAAA-1,autocrine,high,81.64,reviewed +CGGTCAAAAAAAAAAA-1|CTATCAAAAAAAAAAA-1,paracrine,high,80.97,reviewed +CGGTCAAAAAAAAAAA-1|GCAAGAAAAAAAAAAA-1,paracrine,mid,48.51,unreviewed +CGGTCAAAAAAAAAAA-1|GGGTCAAAAAAAAAAA-1,paracrine,mid,66.26,reviewed +CGGTCAAAAAAAAAAA-1|GTATCAAAAAAAAAAA-1,paracrine,high,71.56,unreviewed +CGTACAAAAAAAAAAA-1|AGTACAAAAAAAAAAA-1,paracrine,low,11.69,reviewed +CGTACAAAAAAAAAAA-1|CCCCCAAAAAAAAAAA-1,paracrine,low,26.95,unreviewed +CGTACAAAAAAAAAAA-1|CGTACAAAAAAAAAAA-1,autocrine,low,23.02,reviewed +CGTACAAAAAAAAAAA-1|CTCACAAAAAAAAAAA-1,paracrine,low,12.73,unreviewed +CGTACAAAAAAAAAAA-1|GCCCCAAAAAAAAAAA-1,paracrine,mid,33.56,unreviewed +CGTACAAAAAAAAAAA-1|GGTACAAAAAAAAAAA-1,paracrine,low,30.82,reviewed +CGTACAAAAAAAAAAA-1|GTCACAAAAAAAAAAA-1,paracrine,low,9.07,reviewed +CGTCAAAAAAAAAAAA-1|ACCGAAAAAAAAAAAA-1,paracrine,mid,43.88,unreviewed +CGTCAAAAAAAAAAAA-1|AGTCAAAAAAAAAAAA-1,paracrine,low,33.31,reviewed +CGTCAAAAAAAAAAAA-1|CCCGAAAAAAAAAAAA-1,paracrine,high,75.85,reviewed +CGTCAAAAAAAAAAAA-1|CGTCAAAAAAAAAAAA-1,autocrine,mid,54.26,unreviewed +CGTCAAAAAAAAAAAA-1|GGTCAAAAAAAAAAAA-1,paracrine,mid,64.52,unreviewed +CGTCCAAAAAAAAAAA-1|AGTCCAAAAAAAAAAA-1,paracrine,high,74.48,reviewed +CGTCCAAAAAAAAAAA-1|CCCGCAAAAAAAAAAA-1,paracrine,low,32.22,unreviewed +CGTCCAAAAAAAAAAA-1|CGTCCAAAAAAAAAAA-1,autocrine,high,90.26,unreviewed +CGTCCAAAAAAAAAAA-1|CTCCCAAAAAAAAAAA-1,paracrine,mid,60.05,reviewed +CGTCCAAAAAAAAAAA-1|GCCGCAAAAAAAAAAA-1,paracrine,high,84.47,unreviewed +CGTCCAAAAAAAAAAA-1|GGTCCAAAAAAAAAAA-1,paracrine,high,82.03,unreviewed +CGTCCAAAAAAAAAAA-1|GTCCCAAAAAAAAAAA-1,paracrine,high,73.87,unreviewed +CGTGAAAAAAAAAAAA-1|ACCTAAAAAAAAAAAA-1,paracrine,high,95.13,unreviewed +CGTGAAAAAAAAAAAA-1|AGTGAAAAAAAAAAAA-1,paracrine,low,5.91,unreviewed +CGTGAAAAAAAAAAAA-1|ATCGAAAAAAAAAAAA-1,paracrine,high,76.46,reviewed +CGTGAAAAAAAAAAAA-1|CCCTAAAAAAAAAAAA-1,paracrine,high,94.4,reviewed +CGTGAAAAAAAAAAAA-1|CGTGAAAAAAAAAAAA-1,autocrine,high,97.2,unreviewed +CGTGAAAAAAAAAAAA-1|CTCGAAAAAAAAAAAA-1,paracrine,high,86.97,reviewed +CGTGAAAAAAAAAAAA-1|GGTGAAAAAAAAAAAA-1,paracrine,high,86.42,unreviewed +CGTGCAAAAAAAAAAA-1|AGTGCAAAAAAAAAAA-1,paracrine,low,16.14,unreviewed +CGTGCAAAAAAAAAAA-1|CGTGCAAAAAAAAAAA-1,autocrine,mid,43.24,unreviewed +CGTGCAAAAAAAAAAA-1|CTCGCAAAAAAAAAAA-1,paracrine,high,68.21,unreviewed +CGTGCAAAAAAAAAAA-1|GTCGCAAAAAAAAAAA-1,paracrine,high,85.44,unreviewed +CTAACAAAAAAAAAAA-1|ATAACAAAAAAAAAAA-1,paracrine,mid,41.41,reviewed +CTAACAAAAAAAAAAA-1|CATTAAAAAAAAAAAA-1,paracrine,low,27.62,unreviewed +CTAACAAAAAAAAAAA-1|CGGACAAAAAAAAAAA-1,paracrine,mid,46.65,reviewed +CTAACAAAAAAAAAAA-1|CTAACAAAAAAAAAAA-1,autocrine,low,9.99,unreviewed +CTAACAAAAAAAAAAA-1|GATTAAAAAAAAAAAA-1,paracrine,mid,39.71,unreviewed +CTAACAAAAAAAAAAA-1|GGGACAAAAAAAAAAA-1,paracrine,mid,59.38,unreviewed +CTAACAAAAAAAAAAA-1|GTAACAAAAAAAAAAA-1,paracrine,low,28.05,unreviewed +CTAGCAAAAAAAAAAA-1|AATCCAAAAAAAAAAA-1,paracrine,mid,41.99,unreviewed +CTAGCAAAAAAAAAAA-1|CATCCAAAAAAAAAAA-1,paracrine,mid,58.25,unreviewed +CTAGCAAAAAAAAAAA-1|CTAGCAAAAAAAAAAA-1,autocrine,mid,60.57,unreviewed +CTAGCAAAAAAAAAAA-1|GTAGCAAAAAAAAAAA-1,paracrine,high,67.39,unreviewed +CTATAAAAAAAAAAAA-1|ATATAAAAAAAAAAAA-1,paracrine,low,23.45,unreviewed +CTATAAAAAAAAAAAA-1|CATGAAAAAAAAAAAA-1,paracrine,low,6.7,reviewed +CTATAAAAAAAAAAAA-1|CGGTAAAAAAAAAAAA-1,paracrine,low,20.71,unreviewed +CTATAAAAAAAAAAAA-1|CTATAAAAAAAAAAAA-1,autocrine,low,20.19,unreviewed +CTATAAAAAAAAAAAA-1|GATGAAAAAAAAAAAA-1,paracrine,low,8.1,unreviewed +CTATAAAAAAAAAAAA-1|GGGTAAAAAAAAAAAA-1,paracrine,low,14.07,unreviewed +CTATAAAAAAAAAAAA-1|GTATAAAAAAAAAAAA-1,paracrine,low,10.75,unreviewed +CTATCAAAAAAAAAAA-1|AATGCAAAAAAAAAAA-1,paracrine,high,89.43,unreviewed +CTATCAAAAAAAAAAA-1|AGGTCAAAAAAAAAAA-1,paracrine,high,81.97,unreviewed +CTATCAAAAAAAAAAA-1|ATATCAAAAAAAAAAA-1,paracrine,high,68.64,unreviewed +CTATCAAAAAAAAAAA-1|CATGCAAAAAAAAAAA-1,paracrine,high,93.7,unreviewed +CTATCAAAAAAAAAAA-1|CGGTCAAAAAAAAAAA-1,paracrine,high,91.44,unreviewed +CTATCAAAAAAAAAAA-1|CTATCAAAAAAAAAAA-1,autocrine,high,94.21,unreviewed +CTATCAAAAAAAAAAA-1|GTATCAAAAAAAAAAA-1,paracrine,high,91.93,reviewed +CTCACAAAAAAAAAAA-1|AAAACAAAAAAAAAAA-1,paracrine,high,68.64,reviewed +CTCACAAAAAAAAAAA-1|AGTACAAAAAAAAAAA-1,paracrine,low,15.16,reviewed +CTCACAAAAAAAAAAA-1|ATCACAAAAAAAAAAA-1,paracrine,mid,37.09,reviewed +CTCACAAAAAAAAAAA-1|CAAACAAAAAAAAAAA-1,paracrine,low,31.06,unreviewed +CTCACAAAAAAAAAAA-1|CGTACAAAAAAAAAAA-1,paracrine,low,28.62,unreviewed +CTCACAAAAAAAAAAA-1|CTCACAAAAAAAAAAA-1,autocrine,low,15.83,reviewed +CTCACAAAAAAAAAAA-1|GTCACAAAAAAAAAAA-1,paracrine,low,10.75,unreviewed +CTCCCAAAAAAAAAAA-1|AAACCAAAAAAAAAAA-1,paracrine,mid,47.53,unreviewed +CTCCCAAAAAAAAAAA-1|AGTCCAAAAAAAAAAA-1,paracrine,mid,45.01,reviewed +CTCCCAAAAAAAAAAA-1|ATCCCAAAAAAAAAAA-1,paracrine,high,75.85,unreviewed +CTCCCAAAAAAAAAAA-1|CAACCAAAAAAAAAAA-1,paracrine,mid,37.39,unreviewed +CTCCCAAAAAAAAAAA-1|CGTCCAAAAAAAAAAA-1,paracrine,high,68.97,unreviewed +CTCCCAAAAAAAAAAA-1|CTCCCAAAAAAAAAAA-1,autocrine,low,31.18,unreviewed +CTCCCAAAAAAAAAAA-1|GTCCCAAAAAAAAAAA-1,paracrine,mid,52.44,unreviewed +CTCGAAAAAAAAAAAA-1|ATCGAAAAAAAAAAAA-1,paracrine,low,20.49,unreviewed +CTCGAAAAAAAAAAAA-1|CAAGAAAAAAAAAAAA-1,paracrine,mid,38.09,unreviewed +CTCGAAAAAAAAAAAA-1|CGTGAAAAAAAAAAAA-1,paracrine,mid,55.51,unreviewed +CTCGAAAAAAAAAAAA-1|CTCGAAAAAAAAAAAA-1,autocrine,mid,36.54,unreviewed +CTCGAAAAAAAAAAAA-1|GAAGAAAAAAAAAAAA-1,paracrine,low,29.96,unreviewed +CTCGAAAAAAAAAAAA-1|GGTGAAAAAAAAAAAA-1,paracrine,low,33.19,reviewed +CTCGAAAAAAAAAAAA-1|GTCGAAAAAAAAAAAA-1,paracrine,mid,34.9,unreviewed +CTCGCAAAAAAAAAAA-1|AAAGCAAAAAAAAAAA-1,paracrine,high,68.39,unreviewed +CTCGCAAAAAAAAAAA-1|AGTGCAAAAAAAAAAA-1,paracrine,low,31.36,unreviewed +CTCGCAAAAAAAAAAA-1|ATCGCAAAAAAAAAAA-1,paracrine,high,85.63,reviewed +CTCGCAAAAAAAAAAA-1|CAAGCAAAAAAAAAAA-1,paracrine,high,98.84,unreviewed +CTCGCAAAAAAAAAAA-1|CGTGCAAAAAAAAAAA-1,paracrine,high,72.78,unreviewed +CTCGCAAAAAAAAAAA-1|CTCGCAAAAAAAAAAA-1,autocrine,high,87.18,unreviewed +CTCGCAAAAAAAAAAA-1|GTCGCAAAAAAAAAAA-1,paracrine,high,97.5,reviewed +CTGACAAAAAAAAAAA-1|AACACAAAAAAAAAAA-1,paracrine,mid,44.12,unreviewed +CTGACAAAAAAAAAAA-1|AGACCAAAAAAAAAAA-1,paracrine,low,30.91,unreviewed +CTGACAAAAAAAAAAA-1|ATGACAAAAAAAAAAA-1,paracrine,low,31.0,unreviewed +CTGACAAAAAAAAAAA-1|CACACAAAAAAAAAAA-1,paracrine,high,76.0,unreviewed +CTGACAAAAAAAAAAA-1|CGACCAAAAAAAAAAA-1,paracrine,low,21.29,reviewed +CTGACAAAAAAAAAAA-1|CTGACAAAAAAAAAAA-1,autocrine,mid,53.53,unreviewed +CTGACAAAAAAAAAAA-1|GTGACAAAAAAAAAAA-1,paracrine,low,22.11,unreviewed +CTGGCAAAAAAAAAAA-1|ATGGCAAAAAAAAAAA-1,paracrine,low,14.62,reviewed +CTGGCAAAAAAAAAAA-1|CACGCAAAAAAAAAAA-1,paracrine,low,19.4,unreviewed +CTGGCAAAAAAAAAAA-1|CGATCAAAAAAAAAAA-1,paracrine,low,27.07,unreviewed +CTGGCAAAAAAAAAAA-1|CTGGCAAAAAAAAAAA-1,autocrine,low,27.62,reviewed +CTGGCAAAAAAAAAAA-1|GACGCAAAAAAAAAAA-1,paracrine,low,11.94,reviewed +CTGGCAAAAAAAAAAA-1|GGATCAAAAAAAAAAA-1,paracrine,low,21.92,unreviewed +CTGGCAAAAAAAAAAA-1|GTGGCAAAAAAAAAAA-1,paracrine,low,12.39,unreviewed +CTGTAAAAAAAAAAAA-1|AACTAAAAAAAAAAAA-1,paracrine,low,20.19,unreviewed +CTGTAAAAAAAAAAAA-1|AGAACAAAAAAAAAAA-1,paracrine,mid,51.61,unreviewed +CTGTAAAAAAAAAAAA-1|ATGTAAAAAAAAAAAA-1,paracrine,high,71.92,reviewed +CTGTAAAAAAAAAAAA-1|CACTAAAAAAAAAAAA-1,paracrine,low,12.3,unreviewed +CTGTAAAAAAAAAAAA-1|CGAACAAAAAAAAAAA-1,paracrine,mid,38.79,unreviewed +CTGTAAAAAAAAAAAA-1|CTGTAAAAAAAAAAAA-1,autocrine,mid,63.7,unreviewed +CTGTAAAAAAAAAAAA-1|GTGTAAAAAAAAAAAA-1,paracrine,mid,36.48,unreviewed +CTGTCAAAAAAAAAAA-1|ATGTCAAAAAAAAAAA-1,paracrine,mid,37.76,reviewed +CTGTCAAAAAAAAAAA-1|CACTCAAAAAAAAAAA-1,paracrine,mid,41.6,unreviewed +CTGTCAAAAAAAAAAA-1|CTGTCAAAAAAAAAAA-1,autocrine,mid,56.3,unreviewed +CTGTCAAAAAAAAAAA-1|GACTCAAAAAAAAAAA-1,paracrine,high,69.46,unreviewed +CTGTCAAAAAAAAAAA-1|GTGTCAAAAAAAAAAA-1,paracrine,mid,52.01,reviewed +CTTACAAAAAAAAAAA-1|ATTACAAAAAAAAAAA-1,paracrine,low,10.48,unreviewed +CTTACAAAAAAAAAAA-1|CAGACAAAAAAAAAAA-1,paracrine,mid,33.71,unreviewed +CTTACAAAAAAAAAAA-1|CGCCCAAAAAAAAAAA-1,paracrine,mid,40.26,unreviewed +CTTACAAAAAAAAAAA-1|CTTACAAAAAAAAAAA-1,autocrine,low,8.95,unreviewed +CTTACAAAAAAAAAAA-1|GAGACAAAAAAAAAAA-1,paracrine,low,7.67,unreviewed +CTTACAAAAAAAAAAA-1|GGCCCAAAAAAAAAAA-1,paracrine,low,18.76,reviewed +CTTACAAAAAAAAAAA-1|GTTACAAAAAAAAAAA-1,paracrine,low,18.45,unreviewed +CTTCAAAAAAAAAAAA-1|AAGCAAAAAAAAAAAA-1,paracrine,mid,47.14,unreviewed +CTTCAAAAAAAAAAAA-1|AGCGAAAAAAAAAAAA-1,paracrine,high,68.79,unreviewed +CTTCAAAAAAAAAAAA-1|ATTCAAAAAAAAAAAA-1,paracrine,mid,51.1,reviewed +CTTCAAAAAAAAAAAA-1|CAGCAAAAAAAAAAAA-1,paracrine,low,29.66,unreviewed +CTTCAAAAAAAAAAAA-1|CGCGAAAAAAAAAAAA-1,paracrine,high,77.53,reviewed +CTTCAAAAAAAAAAAA-1|CTTCAAAAAAAAAAAA-1,autocrine,mid,42.69,unreviewed +CTTCAAAAAAAAAAAA-1|GTTCAAAAAAAAAAAA-1,paracrine,low,28.62,unreviewed +CTTCCAAAAAAAAAAA-1|ATTCCAAAAAAAAAAA-1,paracrine,high,88.09,unreviewed +CTTCCAAAAAAAAAAA-1|CAGCCAAAAAAAAAAA-1,paracrine,mid,48.75,unreviewed +CTTCCAAAAAAAAAAA-1|CGCGCAAAAAAAAAAA-1,paracrine,high,83.89,unreviewed +CTTCCAAAAAAAAAAA-1|CTTCCAAAAAAAAAAA-1,autocrine,high,98.6,unreviewed +CTTCCAAAAAAAAAAA-1|GAGCCAAAAAAAAAAA-1,paracrine,high,88.0,unreviewed +CTTCCAAAAAAAAAAA-1|GGCGCAAAAAAAAAAA-1,paracrine,high,76.46,unreviewed +CTTCCAAAAAAAAAAA-1|GTTCCAAAAAAAAAAA-1,paracrine,high,75.43,unreviewed +CTTGAAAAAAAAAAAA-1|AAGGAAAAAAAAAAAA-1,paracrine,mid,47.35,reviewed +CTTGAAAAAAAAAAAA-1|AGCTAAAAAAAAAAAA-1,paracrine,mid,50.85,reviewed +CTTGAAAAAAAAAAAA-1|ATTGAAAAAAAAAAAA-1,paracrine,high,89.52,unreviewed +CTTGAAAAAAAAAAAA-1|CGCTAAAAAAAAAAAA-1,paracrine,high,82.4,reviewed +CTTGAAAAAAAAAAAA-1|CTTGAAAAAAAAAAAA-1,autocrine,high,85.26,unreviewed +GAAACAAAAAAAAAAA-1|CAAACAAAAAAAAAAA-1,paracrine,high,66.99,unreviewed +GAAACAAAAAAAAAAA-1|GAAACAAAAAAAAAAA-1,autocrine,low,28.26,reviewed +GAAACAAAAAAAAAAA-1|GCGTAAAAAAAAAAAA-1,paracrine,low,24.7,unreviewed +GAAACAAAAAAAAAAA-1|GTCACAAAAAAAAAAA-1,paracrine,low,18.27,unreviewed +GAAACAAAAAAAAAAA-1|TAAACAAAAAAAAAAA-1,paracrine,mid,36.6,unreviewed +GAAACAAAAAAAAAAA-1|TCGTAAAAAAAAAAAA-1,paracrine,mid,57.0,unreviewed +GAAACAAAAAAAAAAA-1|TTCACAAAAAAAAAAA-1,paracrine,high,71.77,unreviewed +GAACCAAAAAAAAAAA-1|CAACCAAAAAAAAAAA-1,paracrine,high,80.63,unreviewed +GAACCAAAAAAAAAAA-1|GAACCAAAAAAAAAAA-1,autocrine,mid,58.01,reviewed +GAACCAAAAAAAAAAA-1|GCGACAAAAAAAAAAA-1,paracrine,mid,63.98,reviewed +GAACCAAAAAAAAAAA-1|GTCCCAAAAAAAAAAA-1,paracrine,high,84.47,unreviewed +GAACCAAAAAAAAAAA-1|TAACCAAAAAAAAAAA-1,paracrine,mid,61.81,reviewed +GAACCAAAAAAAAAAA-1|TCGACAAAAAAAAAAA-1,paracrine,high,92.08,reviewed +GAACCAAAAAAAAAAA-1|TTCCCAAAAAAAAAAA-1,paracrine,high,98.33,unreviewed +GAAGAAAAAAAAAAAA-1|CAAGAAAAAAAAAAAA-1,paracrine,mid,33.95,unreviewed +GAAGAAAAAAAAAAAA-1|CTCGAAAAAAAAAAAA-1,paracrine,mid,37.85,unreviewed +GAAGAAAAAAAAAAAA-1|GAAGAAAAAAAAAAAA-1,autocrine,low,28.32,reviewed +GAAGAAAAAAAAAAAA-1|GTCGAAAAAAAAAAAA-1,paracrine,mid,35.38,reviewed +GAAGAAAAAAAAAAAA-1|TAAGAAAAAAAAAAAA-1,paracrine,low,9.38,unreviewed +GAAGCAAAAAAAAAAA-1|CAAGCAAAAAAAAAAA-1,paracrine,high,74.15,reviewed +GAAGCAAAAAAAAAAA-1|GAAGCAAAAAAAAAAA-1,autocrine,low,27.8,reviewed +GAAGCAAAAAAAAAAA-1|GCGCCAAAAAAAAAAA-1,paracrine,mid,56.3,unreviewed +GAAGCAAAAAAAAAAA-1|GTCGCAAAAAAAAAAA-1,paracrine,high,79.96,unreviewed +GAAGCAAAAAAAAAAA-1|TAAGCAAAAAAAAAAA-1,paracrine,low,15.23,reviewed +GAAGCAAAAAAAAAAA-1|TCGCCAAAAAAAAAAA-1,paracrine,low,17.75,reviewed +GACACAAAAAAAAAAA-1|CACACAAAAAAAAAAA-1,paracrine,high,88.64,reviewed +GACACAAAAAAAAAAA-1|GACACAAAAAAAAAAA-1,autocrine,mid,58.86,unreviewed +GACACAAAAAAAAAAA-1|GCTTAAAAAAAAAAAA-1,paracrine,high,76.07,reviewed +GACACAAAAAAAAAAA-1|GTGACAAAAAAAAAAA-1,paracrine,mid,38.49,reviewed +GACACAAAAAAAAAAA-1|TACACAAAAAAAAAAA-1,paracrine,mid,44.64,reviewed +GACACAAAAAAAAAAA-1|TCTTAAAAAAAAAAAA-1,paracrine,mid,41.23,unreviewed +GACACAAAAAAAAAAA-1|TTGACAAAAAAAAAAA-1,paracrine,mid,38.09,reviewed +GACGCAAAAAAAAAAA-1|CACGCAAAAAAAAAAA-1,paracrine,low,25.76,unreviewed +GACGCAAAAAAAAAAA-1|CCTCCAAAAAAAAAAA-1,paracrine,low,20.89,unreviewed +GACGCAAAAAAAAAAA-1|CTGGCAAAAAAAAAAA-1,paracrine,mid,35.2,reviewed +GACGCAAAAAAAAAAA-1|GACGCAAAAAAAAAAA-1,autocrine,low,16.2,reviewed +GACGCAAAAAAAAAAA-1|GCTCCAAAAAAAAAAA-1,paracrine,low,22.78,reviewed +GACGCAAAAAAAAAAA-1|GTGGCAAAAAAAAAAA-1,paracrine,low,12.61,unreviewed +GACGCAAAAAAAAAAA-1|TACGCAAAAAAAAAAA-1,paracrine,low,5.42,reviewed +GACTAAAAAAAAAAAA-1|CACTAAAAAAAAAAAA-1,paracrine,low,31.94,reviewed +GACTAAAAAAAAAAAA-1|GACTAAAAAAAAAAAA-1,autocrine,high,91.26,unreviewed +GACTAAAAAAAAAAAA-1|GCTGAAAAAAAAAAAA-1,paracrine,high,92.2,unreviewed +GACTAAAAAAAAAAAA-1|GTGTAAAAAAAAAAAA-1,paracrine,mid,60.81,reviewed +GACTAAAAAAAAAAAA-1|TACTAAAAAAAAAAAA-1,paracrine,mid,58.47,reviewed +GACTAAAAAAAAAAAA-1|TCTGAAAAAAAAAAAA-1,paracrine,high,81.64,reviewed +GACTAAAAAAAAAAAA-1|TTGTAAAAAAAAAAAA-1,paracrine,high,74.48,reviewed +GACTCAAAAAAAAAAA-1|CACTCAAAAAAAAAAA-1,paracrine,mid,36.39,unreviewed +GACTCAAAAAAAAAAA-1|CCTGCAAAAAAAAAAA-1,paracrine,low,23.36,unreviewed +GACTCAAAAAAAAAAA-1|CTGTCAAAAAAAAAAA-1,paracrine,mid,49.39,reviewed +GACTCAAAAAAAAAAA-1|GACTCAAAAAAAAAAA-1,autocrine,high,72.96,reviewed +GACTCAAAAAAAAAAA-1|GCTGCAAAAAAAAAAA-1,paracrine,high,74.76,unreviewed +GACTCAAAAAAAAAAA-1|GTGTCAAAAAAAAAAA-1,paracrine,mid,53.05,unreviewed +GACTCAAAAAAAAAAA-1|TACTCAAAAAAAAAAA-1,paracrine,mid,57.43,reviewed +GAGACAAAAAAAAAAA-1|CAGACAAAAAAAAAAA-1,paracrine,low,25.21,unreviewed +GAGACAAAAAAAAAAA-1|CCAACAAAAAAAAAAA-1,paracrine,low,8.34,unreviewed +GAGACAAAAAAAAAAA-1|CTTACAAAAAAAAAAA-1,paracrine,low,9.2,reviewed +GAGACAAAAAAAAAAA-1|GAGACAAAAAAAAAAA-1,autocrine,low,6.76,reviewed +GAGACAAAAAAAAAAA-1|GCAACAAAAAAAAAAA-1,paracrine,low,19.98,unreviewed +GAGACAAAAAAAAAAA-1|GTTACAAAAAAAAAAA-1,paracrine,low,13.46,unreviewed +GAGACAAAAAAAAAAA-1|TAGACAAAAAAAAAAA-1,paracrine,low,5.6,unreviewed +GAGCAAAAAAAAAAAA-1|CAGCAAAAAAAAAAAA-1,paracrine,mid,34.1,unreviewed +GAGCAAAAAAAAAAAA-1|GAGCAAAAAAAAAAAA-1,autocrine,mid,43.64,unreviewed +GAGCAAAAAAAAAAAA-1|GTTCAAAAAAAAAAAA-1,paracrine,mid,35.38,reviewed +GAGCAAAAAAAAAAAA-1|TAGCAAAAAAAAAAAA-1,paracrine,low,28.05,unreviewed +GAGCAAAAAAAAAAAA-1|TTTCAAAAAAAAAAAA-1,paracrine,mid,38.25,unreviewed +GAGCCAAAAAAAAAAA-1|CAGCCAAAAAAAAAAA-1,paracrine,low,11.33,unreviewed +GAGCCAAAAAAAAAAA-1|CCACCAAAAAAAAAAA-1,paracrine,mid,41.81,unreviewed +GAGCCAAAAAAAAAAA-1|CTTCCAAAAAAAAAAA-1,paracrine,high,67.75,unreviewed +GAGCCAAAAAAAAAAA-1|GAGCCAAAAAAAAAAA-1,autocrine,mid,39.13,unreviewed +GAGCCAAAAAAAAAAA-1|GCACCAAAAAAAAAAA-1,paracrine,mid,39.34,reviewed +GAGCCAAAAAAAAAAA-1|GTTCCAAAAAAAAAAA-1,paracrine,low,23.23,unreviewed +GAGCCAAAAAAAAAAA-1|TAGCCAAAAAAAAAAA-1,paracrine,mid,41.72,reviewed +GATCCAAAAAAAAAAA-1|CATCCAAAAAAAAAAA-1,paracrine,mid,35.66,unreviewed +GATCCAAAAAAAAAAA-1|GATCCAAAAAAAAAAA-1,autocrine,mid,47.14,unreviewed +GATCCAAAAAAAAAAA-1|GCCCCAAAAAAAAAAA-1,paracrine,mid,39.13,unreviewed +GATCCAAAAAAAAAAA-1|GTAGCAAAAAAAAAAA-1,paracrine,mid,49.97,reviewed +GATCCAAAAAAAAAAA-1|TATCCAAAAAAAAAAA-1,paracrine,mid,34.71,unreviewed +GATCCAAAAAAAAAAA-1|TCCCCAAAAAAAAAAA-1,paracrine,low,8.16,unreviewed +GATCCAAAAAAAAAAA-1|TTAGCAAAAAAAAAAA-1,paracrine,low,13.58,unreviewed +GATGAAAAAAAAAAAA-1|CATGAAAAAAAAAAAA-1,paracrine,low,11.81,reviewed +GATGAAAAAAAAAAAA-1|CCCGAAAAAAAAAAAA-1,paracrine,high,72.41,unreviewed +GATGAAAAAAAAAAAA-1|CTATAAAAAAAAAAAA-1,paracrine,mid,52.98,unreviewed +GATGAAAAAAAAAAAA-1|GATGAAAAAAAAAAAA-1,autocrine,low,15.38,unreviewed +GATGAAAAAAAAAAAA-1|GCCGAAAAAAAAAAAA-1,paracrine,mid,49.82,unreviewed +GATGAAAAAAAAAAAA-1|GTATAAAAAAAAAAAA-1,paracrine,low,24.12,reviewed +GATGAAAAAAAAAAAA-1|TATGAAAAAAAAAAAA-1,paracrine,low,8.04,unreviewed +GATGCAAAAAAAAAAA-1|CATGCAAAAAAAAAAA-1,paracrine,high,97.26,unreviewed +GATGCAAAAAAAAAAA-1|GATGCAAAAAAAAAAA-1,autocrine,high,99.63,unreviewed +GATGCAAAAAAAAAAA-1|GCCGCAAAAAAAAAAA-1,paracrine,high,94.85,unreviewed +GATGCAAAAAAAAAAA-1|GTATCAAAAAAAAAAA-1,paracrine,high,96.32,unreviewed +GATGCAAAAAAAAAAA-1|TATGCAAAAAAAAAAA-1,paracrine,high,98.96,unreviewed +GATGCAAAAAAAAAAA-1|TCCGCAAAAAAAAAAA-1,paracrine,high,93.85,unreviewed +GATGCAAAAAAAAAAA-1|TTATCAAAAAAAAAAA-1,paracrine,high,70.8,unreviewed +GATTAAAAAAAAAAAA-1|CATTAAAAAAAAAAAA-1,paracrine,high,72.08,unreviewed +GATTAAAAAAAAAAAA-1|CCCTAAAAAAAAAAAA-1,paracrine,high,91.93,unreviewed +GATTAAAAAAAAAAAA-1|CTAACAAAAAAAAAAA-1,paracrine,low,30.21,unreviewed +GATTAAAAAAAAAAAA-1|GATTAAAAAAAAAAAA-1,autocrine,high,84.14,unreviewed +GATTAAAAAAAAAAAA-1|GCCTAAAAAAAAAAAA-1,paracrine,high,92.75,reviewed +GATTAAAAAAAAAAAA-1|GTAACAAAAAAAAAAA-1,paracrine,high,66.75,reviewed +GATTAAAAAAAAAAAA-1|TATTAAAAAAAAAAAA-1,paracrine,high,97.44,unreviewed +GCAACAAAAAAAAAAA-1|CCAACAAAAAAAAAAA-1,paracrine,mid,50.94,unreviewed +GCAACAAAAAAAAAAA-1|GAGACAAAAAAAAAAA-1,paracrine,mid,33.95,reviewed +GCAACAAAAAAAAAAA-1|GCAACAAAAAAAAAAA-1,autocrine,high,81.12,reviewed +GCAACAAAAAAAAAAA-1|GGGTAAAAAAAAAAAA-1,paracrine,mid,53.81,unreviewed +GCAACAAAAAAAAAAA-1|TAGACAAAAAAAAAAA-1,paracrine,low,15.29,unreviewed +GCAACAAAAAAAAAAA-1|TCAACAAAAAAAAAAA-1,paracrine,low,32.46,reviewed +GCAACAAAAAAAAAAA-1|TGGTAAAAAAAAAAAA-1,paracrine,low,6.15,unreviewed +GCAAGAAAAAAAAAAA-1|CCAAGAAAAAAAAAAA-1,paracrine,low,14.95,unreviewed +GCAAGAAAAAAAAAAA-1|CGGTCAAAAAAAAAAA-1,paracrine,mid,38.49,reviewed +GCAAGAAAAAAAAAAA-1|GCAAGAAAAAAAAAAA-1,autocrine,low,13.52,reviewed +GCAAGAAAAAAAAAAA-1|GGGTCAAAAAAAAAAA-1,paracrine,low,22.59,reviewed +GCAAGAAAAAAAAAAA-1|TCAAGAAAAAAAAAAA-1,paracrine,low,12.0,unreviewed +GCACCAAAAAAAAAAA-1|CCACCAAAAAAAAAAA-1,paracrine,low,20.65,unreviewed +GCACCAAAAAAAAAAA-1|GAGCCAAAAAAAAAAA-1,paracrine,low,26.67,unreviewed +GCACCAAAAAAAAAAA-1|GCACCAAAAAAAAAAA-1,autocrine,low,22.38,unreviewed +GCACCAAAAAAAAAAA-1|GGGACAAAAAAAAAAA-1,paracrine,low,30.09,unreviewed +GCACCAAAAAAAAAAA-1|TAGCCAAAAAAAAAAA-1,paracrine,low,22.72,reviewed +GCACCAAAAAAAAAAA-1|TCACCAAAAAAAAAAA-1,paracrine,low,14.13,unreviewed +GCACCAAAAAAAAAAA-1|TGGACAAAAAAAAAAA-1,paracrine,low,7.43,reviewed +GCCCCAAAAAAAAAAA-1|CATCCAAAAAAAAAAA-1,paracrine,high,78.44,unreviewed +GCCCCAAAAAAAAAAA-1|CCCCCAAAAAAAAAAA-1,paracrine,high,74.0,unreviewed +GCCCCAAAAAAAAAAA-1|CGTACAAAAAAAAAAA-1,paracrine,high,70.04,unreviewed +GCCCCAAAAAAAAAAA-1|GATCCAAAAAAAAAAA-1,paracrine,high,82.49,unreviewed +GCCCCAAAAAAAAAAA-1|GCCCCAAAAAAAAAAA-1,autocrine,high,72.84,reviewed +GCCCCAAAAAAAAAAA-1|GGTACAAAAAAAAAAA-1,paracrine,high,77.62,unreviewed +GCCCCAAAAAAAAAAA-1|TCCCCAAAAAAAAAAA-1,paracrine,low,22.9,reviewed +GCCGAAAAAAAAAAAA-1|CCCGAAAAAAAAAAAA-1,paracrine,mid,61.02,unreviewed +GCCGAAAAAAAAAAAA-1|GATGAAAAAAAAAAAA-1,paracrine,low,11.27,unreviewed +GCCGAAAAAAAAAAAA-1|GCCGAAAAAAAAAAAA-1,autocrine,mid,34.38,unreviewed +GCCGAAAAAAAAAAAA-1|GGTCAAAAAAAAAAAA-1,paracrine,mid,43.64,unreviewed +GCCGAAAAAAAAAAAA-1|TATGAAAAAAAAAAAA-1,paracrine,low,7.86,unreviewed +GCCGAAAAAAAAAAAA-1|TCCGAAAAAAAAAAAA-1,paracrine,low,5.79,unreviewed +GCCGAAAAAAAAAAAA-1|TGTCAAAAAAAAAAAA-1,paracrine,low,7.13,unreviewed +GCCGCAAAAAAAAAAA-1|CATGCAAAAAAAAAAA-1,paracrine,high,96.5,unreviewed +GCCGCAAAAAAAAAAA-1|CCCGCAAAAAAAAAAA-1,paracrine,mid,45.83,unreviewed +GCCGCAAAAAAAAAAA-1|CGTCCAAAAAAAAAAA-1,paracrine,high,96.41,unreviewed +GCCGCAAAAAAAAAAA-1|GATGCAAAAAAAAAAA-1,paracrine,high,97.87,reviewed +GCCGCAAAAAAAAAAA-1|GCCGCAAAAAAAAAAA-1,autocrine,high,96.22,unreviewed +GCCGCAAAAAAAAAAA-1|GGTCCAAAAAAAAAAA-1,paracrine,high,93.24,unreviewed +GCCGCAAAAAAAAAAA-1|TCCGCAAAAAAAAAAA-1,paracrine,high,89.68,unreviewed +GCCTAAAAAAAAAAAA-1|CCCTAAAAAAAAAAAA-1,paracrine,high,85.9,reviewed +GCCTAAAAAAAAAAAA-1|GATTAAAAAAAAAAAA-1,paracrine,high,68.79,unreviewed +GCCTAAAAAAAAAAAA-1|GCCTAAAAAAAAAAAA-1,autocrine,high,87.18,reviewed +GCCTAAAAAAAAAAAA-1|GGTGAAAAAAAAAAAA-1,paracrine,high,76.98,unreviewed +GCCTAAAAAAAAAAAA-1|TATTAAAAAAAAAAAA-1,paracrine,high,92.54,unreviewed +GCCTAAAAAAAAAAAA-1|TCCTAAAAAAAAAAAA-1,paracrine,high,71.19,unreviewed +GCCTAAAAAAAAAAAA-1|TGTGAAAAAAAAAAAA-1,paracrine,mid,62.79,unreviewed +GCGACAAAAAAAAAAA-1|CAACCAAAAAAAAAAA-1,paracrine,mid,53.81,unreviewed +GCGACAAAAAAAAAAA-1|CCGACAAAAAAAAAAA-1,paracrine,mid,52.77,reviewed +GCGACAAAAAAAAAAA-1|CGAACAAAAAAAAAAA-1,paracrine,mid,47.62,unreviewed +GCGACAAAAAAAAAAA-1|GAACCAAAAAAAAAAA-1,paracrine,low,32.22,unreviewed +GCGACAAAAAAAAAAA-1|GCGACAAAAAAAAAAA-1,autocrine,mid,50.37,unreviewed +GCGACAAAAAAAAAAA-1|GGAACAAAAAAAAAAA-1,paracrine,mid,37.0,reviewed +GCGACAAAAAAAAAAA-1|TCGACAAAAAAAAAAA-1,paracrine,high,76.89,reviewed +GCGCCAAAAAAAAAAA-1|CAAGCAAAAAAAAAAA-1,paracrine,high,99.15,reviewed +GCGCCAAAAAAAAAAA-1|CCGCCAAAAAAAAAAA-1,paracrine,high,97.93,unreviewed +GCGCCAAAAAAAAAAA-1|CGACCAAAAAAAAAAA-1,paracrine,high,75.43,unreviewed +GCGCCAAAAAAAAAAA-1|GAAGCAAAAAAAAAAA-1,paracrine,high,77.62,unreviewed +GCGCCAAAAAAAAAAA-1|GCGCCAAAAAAAAAAA-1,autocrine,high,97.32,unreviewed +GCGCCAAAAAAAAAAA-1|GGACCAAAAAAAAAAA-1,paracrine,high,99.51,unreviewed +GCGCCAAAAAAAAAAA-1|TCGCCAAAAAAAAAAA-1,paracrine,high,71.04,reviewed +GCGTAAAAAAAAAAAA-1|CAAACAAAAAAAAAAA-1,paracrine,low,14.37,unreviewed +GCGTAAAAAAAAAAAA-1|CCGTAAAAAAAAAAAA-1,paracrine,low,18.51,unreviewed +GCGTAAAAAAAAAAAA-1|CGATAAAAAAAAAAAA-1,paracrine,low,12.06,unreviewed +GCGTAAAAAAAAAAAA-1|GAAACAAAAAAAAAAA-1,paracrine,low,7.37,unreviewed +GCGTAAAAAAAAAAAA-1|GCGTAAAAAAAAAAAA-1,autocrine,low,8.4,unreviewed +GCGTAAAAAAAAAAAA-1|GGATAAAAAAAAAAAA-1,paracrine,low,9.62,unreviewed +GCGTAAAAAAAAAAAA-1|TCGTAAAAAAAAAAAA-1,paracrine,low,16.87,reviewed +GCGTCAAAAAAAAAAA-1|CCGTCAAAAAAAAAAA-1,paracrine,mid,45.49,unreviewed +GCGTCAAAAAAAAAAA-1|GCGTCAAAAAAAAAAA-1,autocrine,high,78.71,unreviewed +GCGTCAAAAAAAAAAA-1|GGATCAAAAAAAAAAA-1,paracrine,mid,65.41,reviewed +GCGTCAAAAAAAAAAA-1|TAAAGAAAAAAAAAAA-1,paracrine,low,6.39,unreviewed +GCGTCAAAAAAAAAAA-1|TCGTCAAAAAAAAAAA-1,paracrine,low,9.26,unreviewed +GCGTCAAAAAAAAAAA-1|TGATCAAAAAAAAAAA-1,paracrine,low,12.18,unreviewed +GCTCCAAAAAAAAAAA-1|CCTCCAAAAAAAAAAA-1,paracrine,low,26.55,unreviewed +GCTCCAAAAAAAAAAA-1|GACGCAAAAAAAAAAA-1,paracrine,low,21.74,unreviewed +GCTCCAAAAAAAAAAA-1|GCTCCAAAAAAAAAAA-1,autocrine,mid,36.39,reviewed +GCTCCAAAAAAAAAAA-1|GGCCCAAAAAAAAAAA-1,paracrine,mid,39.86,reviewed +GCTCCAAAAAAAAAAA-1|TACGCAAAAAAAAAAA-1,paracrine,low,5.85,unreviewed +GCTCCAAAAAAAAAAA-1|TCTCCAAAAAAAAAAA-1,paracrine,low,5.18,unreviewed +GCTCCAAAAAAAAAAA-1|TGCCCAAAAAAAAAAA-1,paracrine,low,5.05,unreviewed +GCTGAAAAAAAAAAAA-1|CACTAAAAAAAAAAAA-1,paracrine,mid,35.84,unreviewed +GCTGAAAAAAAAAAAA-1|CCTGAAAAAAAAAAAA-1,paracrine,high,94.09,reviewed +GCTGAAAAAAAAAAAA-1|CGCGAAAAAAAAAAAA-1,paracrine,high,96.59,reviewed +GCTGAAAAAAAAAAAA-1|GACTAAAAAAAAAAAA-1,paracrine,high,96.04,unreviewed +GCTGAAAAAAAAAAAA-1|GCTGAAAAAAAAAAAA-1,autocrine,high,95.74,unreviewed +GCTGAAAAAAAAAAAA-1|GGCGAAAAAAAAAAAA-1,paracrine,mid,45.49,unreviewed +GCTGAAAAAAAAAAAA-1|TCTGAAAAAAAAAAAA-1,paracrine,high,85.57,unreviewed +GCTGCAAAAAAAAAAA-1|CCTGCAAAAAAAAAAA-1,paracrine,mid,57.16,reviewed +GCTGCAAAAAAAAAAA-1|GACTCAAAAAAAAAAA-1,paracrine,high,90.41,unreviewed +GCTGCAAAAAAAAAAA-1|GCTGCAAAAAAAAAAA-1,autocrine,high,89.43,reviewed +GCTGCAAAAAAAAAAA-1|GGCGCAAAAAAAAAAA-1,paracrine,mid,55.97,unreviewed +GCTGCAAAAAAAAAAA-1|TACTCAAAAAAAAAAA-1,paracrine,high,78.5,reviewed +GCTGCAAAAAAAAAAA-1|TCTGCAAAAAAAAAAA-1,paracrine,high,83.07,unreviewed +GCTGCAAAAAAAAAAA-1|TGCGCAAAAAAAAAAA-1,paracrine,high,69.61,unreviewed +GCTTAAAAAAAAAAAA-1|CACACAAAAAAAAAAA-1,paracrine,high,86.66,reviewed +GCTTAAAAAAAAAAAA-1|CCTTAAAAAAAAAAAA-1,paracrine,high,87.58,reviewed +GCTTAAAAAAAAAAAA-1|CGCTAAAAAAAAAAAA-1,paracrine,mid,64.74,reviewed +GCTTAAAAAAAAAAAA-1|GACACAAAAAAAAAAA-1,paracrine,mid,62.61,unreviewed +GCTTAAAAAAAAAAAA-1|GCTTAAAAAAAAAAAA-1,autocrine,high,84.32,unreviewed +GCTTAAAAAAAAAAAA-1|GGCTAAAAAAAAAAAA-1,paracrine,mid,46.29,reviewed +GCTTAAAAAAAAAAAA-1|TCTTAAAAAAAAAAAA-1,paracrine,mid,36.81,reviewed +GGAACAAAAAAAAAAA-1|CGAACAAAAAAAAAAA-1,paracrine,mid,64.86,unreviewed +GGAACAAAAAAAAAAA-1|GCGACAAAAAAAAAAA-1,paracrine,mid,48.51,unreviewed +GGAACAAAAAAAAAAA-1|GGAACAAAAAAAAAAA-1,autocrine,mid,54.14,unreviewed +GGAACAAAAAAAAAAA-1|GTGTAAAAAAAAAAAA-1,paracrine,mid,59.1,reviewed +GGAACAAAAAAAAAAA-1|TCGACAAAAAAAAAAA-1,paracrine,high,85.08,unreviewed +GGAACAAAAAAAAAAA-1|TGAACAAAAAAAAAAA-1,paracrine,high,83.74,unreviewed +GGAACAAAAAAAAAAA-1|TTGTAAAAAAAAAAAA-1,paracrine,mid,64.86,unreviewed +GGACCAAAAAAAAAAA-1|CGACCAAAAAAAAAAA-1,paracrine,mid,58.77,unreviewed +GGACCAAAAAAAAAAA-1|GCGCCAAAAAAAAAAA-1,paracrine,high,88.09,unreviewed +GGACCAAAAAAAAAAA-1|GGACCAAAAAAAAAAA-1,autocrine,high,97.38,unreviewed +GGACCAAAAAAAAAAA-1|GTGACAAAAAAAAAAA-1,paracrine,mid,63.0,unreviewed +GGACCAAAAAAAAAAA-1|TCGCCAAAAAAAAAAA-1,paracrine,mid,42.54,unreviewed +GGACCAAAAAAAAAAA-1|TGACCAAAAAAAAAAA-1,paracrine,mid,38.37,reviewed +GGACCAAAAAAAAAAA-1|TTGACAAAAAAAAAAA-1,paracrine,mid,58.47,reviewed +GGATAAAAAAAAAAAA-1|CGATAAAAAAAAAAAA-1,paracrine,mid,34.96,reviewed +GGATAAAAAAAAAAAA-1|GCGTAAAAAAAAAAAA-1,paracrine,low,19.21,unreviewed +GGATAAAAAAAAAAAA-1|GGATAAAAAAAAAAAA-1,autocrine,low,20.1,unreviewed +GGATAAAAAAAAAAAA-1|GTGGAAAAAAAAAAAA-1,paracrine,low,18.39,unreviewed +GGATAAAAAAAAAAAA-1|TCGTAAAAAAAAAAAA-1,paracrine,mid,38.95,unreviewed +GGATAAAAAAAAAAAA-1|TGATAAAAAAAAAAAA-1,paracrine,low,29.45,reviewed +GGATAAAAAAAAAAAA-1|TTGGAAAAAAAAAAAA-1,paracrine,low,14.95,unreviewed +GGATCAAAAAAAAAAA-1|CCGTCAAAAAAAAAAA-1,paracrine,low,24.42,unreviewed +GGATCAAAAAAAAAAA-1|CGATCAAAAAAAAAAA-1,paracrine,mid,36.18,reviewed +GGATCAAAAAAAAAAA-1|CTGGCAAAAAAAAAAA-1,paracrine,mid,42.54,unreviewed +GGATCAAAAAAAAAAA-1|GCGTCAAAAAAAAAAA-1,paracrine,mid,52.62,unreviewed +GGATCAAAAAAAAAAA-1|GGATCAAAAAAAAAAA-1,autocrine,mid,35.66,unreviewed +GGATCAAAAAAAAAAA-1|GTGGCAAAAAAAAAAA-1,paracrine,low,17.6,unreviewed +GGATCAAAAAAAAAAA-1|TGATCAAAAAAAAAAA-1,paracrine,low,5.54,unreviewed +GGCCCAAAAAAAAAAA-1|CCTCCAAAAAAAAAAA-1,paracrine,mid,42.97,reviewed +GGCCCAAAAAAAAAAA-1|CGCCCAAAAAAAAAAA-1,paracrine,high,74.15,unreviewed +GGCCCAAAAAAAAAAA-1|CTTACAAAAAAAAAAA-1,paracrine,low,20.89,unreviewed +GGCCCAAAAAAAAAAA-1|GCTCCAAAAAAAAAAA-1,paracrine,mid,64.52,unreviewed +GGCCCAAAAAAAAAAA-1|GGCCCAAAAAAAAAAA-1,autocrine,mid,48.63,unreviewed +GGCCCAAAAAAAAAAA-1|GTTACAAAAAAAAAAA-1,paracrine,mid,43.06,unreviewed +GGCCCAAAAAAAAAAA-1|TGCCCAAAAAAAAAAA-1,paracrine,low,5.97,unreviewed +GGCGAAAAAAAAAAAA-1|CGCGAAAAAAAAAAAA-1,paracrine,mid,51.46,unreviewed +GGCGAAAAAAAAAAAA-1|GCTGAAAAAAAAAAAA-1,paracrine,mid,40.71,unreviewed +GGCGAAAAAAAAAAAA-1|GGCGAAAAAAAAAAAA-1,autocrine,low,10.11,reviewed +GGCGAAAAAAAAAAAA-1|GTTCAAAAAAAAAAAA-1,paracrine,low,13.31,reviewed +GGCGAAAAAAAAAAAA-1|TCTGAAAAAAAAAAAA-1,paracrine,low,28.93,unreviewed +GGCGAAAAAAAAAAAA-1|TGCGAAAAAAAAAAAA-1,paracrine,mid,51.74,unreviewed +GGCGAAAAAAAAAAAA-1|TTTCAAAAAAAAAAAA-1,paracrine,low,18.0,unreviewed +GGCGCAAAAAAAAAAA-1|CCTGCAAAAAAAAAAA-1,paracrine,mid,37.39,unreviewed +GGCGCAAAAAAAAAAA-1|CGCGCAAAAAAAAAAA-1,paracrine,mid,51.89,unreviewed +GGCGCAAAAAAAAAAA-1|CTTCCAAAAAAAAAAA-1,paracrine,high,82.76,unreviewed +GGCGCAAAAAAAAAAA-1|GCTGCAAAAAAAAAAA-1,paracrine,high,73.23,unreviewed +GGCGCAAAAAAAAAAA-1|GGCGCAAAAAAAAAAA-1,autocrine,low,29.78,reviewed +GGCGCAAAAAAAAAAA-1|GTTCCAAAAAAAAAAA-1,paracrine,low,25.49,reviewed +GGCGCAAAAAAAAAAA-1|TGCGCAAAAAAAAAAA-1,paracrine,mid,48.29,unreviewed +GGCTAAAAAAAAAAAA-1|CGCTAAAAAAAAAAAA-1,paracrine,high,66.66,reviewed +GGCTAAAAAAAAAAAA-1|GCTTAAAAAAAAAAAA-1,paracrine,high,88.73,reviewed +GGCTAAAAAAAAAAAA-1|GGCTAAAAAAAAAAAA-1,autocrine,mid,45.83,reviewed +GGCTAAAAAAAAAAAA-1|TCTTAAAAAAAAAAAA-1,paracrine,mid,61.75,reviewed +GGGACAAAAAAAAAAA-1|CCACCAAAAAAAAAAA-1,paracrine,mid,60.48,reviewed +GGGACAAAAAAAAAAA-1|CGGACAAAAAAAAAAA-1,paracrine,mid,45.25,reviewed +GGGACAAAAAAAAAAA-1|CTAACAAAAAAAAAAA-1,paracrine,low,10.41,unreviewed +GGGACAAAAAAAAAAA-1|GCACCAAAAAAAAAAA-1,paracrine,mid,56.15,unreviewed +GGGACAAAAAAAAAAA-1|GGGACAAAAAAAAAAA-1,autocrine,mid,65.47,reviewed +GGGACAAAAAAAAAAA-1|GTAACAAAAAAAAAAA-1,paracrine,low,25.33,unreviewed +GGGACAAAAAAAAAAA-1|TGGACAAAAAAAAAAA-1,paracrine,low,11.63,reviewed +GGGGCAAAAAAAAAAA-1|GGGGCAAAAAAAAAAA-1,autocrine,mid,38.25,reviewed +GGGGCAAAAAAAAAAA-1|GTAGCAAAAAAAAAAA-1,paracrine,high,77.74,unreviewed +GGGGCAAAAAAAAAAA-1|TCATCAAAAAAAAAAA-1,paracrine,mid,42.81,unreviewed +GGGGCAAAAAAAAAAA-1|TGGGCAAAAAAAAAAA-1,paracrine,mid,54.84,unreviewed +GGGGCAAAAAAAAAAA-1|TTAGCAAAAAAAAAAA-1,paracrine,low,27.62,unreviewed +GGGTAAAAAAAAAAAA-1|CCAACAAAAAAAAAAA-1,paracrine,low,25.33,reviewed +GGGTAAAAAAAAAAAA-1|CGGTAAAAAAAAAAAA-1,paracrine,low,32.37,reviewed +GGGTAAAAAAAAAAAA-1|CTATAAAAAAAAAAAA-1,paracrine,low,22.66,unreviewed +GGGTAAAAAAAAAAAA-1|GCAACAAAAAAAAAAA-1,paracrine,low,31.76,reviewed +GGGTAAAAAAAAAAAA-1|GGGTAAAAAAAAAAAA-1,autocrine,low,25.67,reviewed +GGGTAAAAAAAAAAAA-1|GTATAAAAAAAAAAAA-1,paracrine,low,15.96,reviewed +GGGTAAAAAAAAAAAA-1|TGGTAAAAAAAAAAAA-1,paracrine,low,4.63,unreviewed +GGGTCAAAAAAAAAAA-1|CGGTCAAAAAAAAAAA-1,paracrine,high,78.56,unreviewed +GGGTCAAAAAAAAAAA-1|GCAAGAAAAAAAAAAA-1,paracrine,mid,37.39,unreviewed +GGGTCAAAAAAAAAAA-1|GGGTCAAAAAAAAAAA-1,autocrine,mid,54.42,unreviewed +GGGTCAAAAAAAAAAA-1|GTATCAAAAAAAAAAA-1,paracrine,mid,63.15,reviewed +GGGTCAAAAAAAAAAA-1|TCAAGAAAAAAAAAAA-1,paracrine,low,30.21,reviewed +GGGTCAAAAAAAAAAA-1|TGGTCAAAAAAAAAAA-1,paracrine,low,8.89,unreviewed +GGGTCAAAAAAAAAAA-1|TTATCAAAAAAAAAAA-1,paracrine,mid,38.95,unreviewed +GGTACAAAAAAAAAAA-1|CGTACAAAAAAAAAAA-1,paracrine,mid,43.64,unreviewed +GGTACAAAAAAAAAAA-1|GCCCCAAAAAAAAAAA-1,paracrine,mid,54.57,unreviewed +GGTACAAAAAAAAAAA-1|GGTACAAAAAAAAAAA-1,autocrine,mid,57.43,unreviewed +GGTACAAAAAAAAAAA-1|GTCACAAAAAAAAAAA-1,paracrine,low,14.68,unreviewed +GGTACAAAAAAAAAAA-1|TCCCCAAAAAAAAAAA-1,paracrine,low,12.97,unreviewed +GGTACAAAAAAAAAAA-1|TGTACAAAAAAAAAAA-1,paracrine,low,19.55,unreviewed +GGTACAAAAAAAAAAA-1|TTCACAAAAAAAAAAA-1,paracrine,mid,64.16,reviewed +GGTCAAAAAAAAAAAA-1|CCCGAAAAAAAAAAAA-1,paracrine,mid,37.18,unreviewed +GGTCAAAAAAAAAAAA-1|CGTCAAAAAAAAAAAA-1,paracrine,low,20.28,unreviewed +GGTCAAAAAAAAAAAA-1|GCCGAAAAAAAAAAAA-1,paracrine,low,18.21,unreviewed +GGTCAAAAAAAAAAAA-1|GGTCAAAAAAAAAAAA-1,autocrine,low,26.43,unreviewed +GGTCAAAAAAAAAAAA-1|TGTCAAAAAAAAAAAA-1,paracrine,low,5.72,unreviewed +GGTCCAAAAAAAAAAA-1|CGTCCAAAAAAAAAAA-1,paracrine,high,96.77,unreviewed +GGTCCAAAAAAAAAAA-1|GCCGCAAAAAAAAAAA-1,paracrine,high,93.03,unreviewed +GGTCCAAAAAAAAAAA-1|GGTCCAAAAAAAAAAA-1,autocrine,high,93.3,unreviewed +GGTCCAAAAAAAAAAA-1|GTCCCAAAAAAAAAAA-1,paracrine,high,86.08,reviewed +GGTCCAAAAAAAAAAA-1|TCCGCAAAAAAAAAAA-1,paracrine,high,89.22,unreviewed +GGTCCAAAAAAAAAAA-1|TGTCCAAAAAAAAAAA-1,paracrine,high,67.87,unreviewed +GGTCCAAAAAAAAAAA-1|TTCCCAAAAAAAAAAA-1,paracrine,high,97.62,unreviewed +GGTGAAAAAAAAAAAA-1|CCCTAAAAAAAAAAAA-1,paracrine,mid,44.7,unreviewed +GGTGAAAAAAAAAAAA-1|CGTGAAAAAAAAAAAA-1,paracrine,mid,44.82,reviewed +GGTGAAAAAAAAAAAA-1|CTCGAAAAAAAAAAAA-1,paracrine,mid,34.26,reviewed +GGTGAAAAAAAAAAAA-1|GCCTAAAAAAAAAAAA-1,paracrine,mid,42.2,unreviewed +GGTGAAAAAAAAAAAA-1|GGTGAAAAAAAAAAAA-1,autocrine,low,23.72,unreviewed +GGTGAAAAAAAAAAAA-1|GTCGAAAAAAAAAAAA-1,paracrine,low,32.22,reviewed +GGTGAAAAAAAAAAAA-1|TGTGAAAAAAAAAAAA-1,paracrine,low,18.91,reviewed +GTAACAAAAAAAAAAA-1|CTAACAAAAAAAAAAA-1,paracrine,low,9.81,unreviewed +GTAACAAAAAAAAAAA-1|GATTAAAAAAAAAAAA-1,paracrine,mid,35.2,unreviewed +GTAACAAAAAAAAAAA-1|GGGACAAAAAAAAAAA-1,paracrine,mid,41.05,reviewed +GTAACAAAAAAAAAAA-1|GTAACAAAAAAAAAAA-1,autocrine,low,14.43,unreviewed +GTAACAAAAAAAAAAA-1|TATTAAAAAAAAAAAA-1,paracrine,high,67.97,unreviewed +GTAACAAAAAAAAAAA-1|TGGACAAAAAAAAAAA-1,paracrine,low,9.68,unreviewed +GTAACAAAAAAAAAAA-1|TTAACAAAAAAAAAAA-1,paracrine,low,15.68,unreviewed +GTAGCAAAAAAAAAAA-1|CATCCAAAAAAAAAAA-1,paracrine,high,86.75,unreviewed +GTAGCAAAAAAAAAAA-1|CTAGCAAAAAAAAAAA-1,paracrine,high,95.37,reviewed +GTAGCAAAAAAAAAAA-1|GATCCAAAAAAAAAAA-1,paracrine,high,90.16,reviewed +GTAGCAAAAAAAAAAA-1|GGGGCAAAAAAAAAAA-1,paracrine,high,70.37,unreviewed +GTAGCAAAAAAAAAAA-1|GTAGCAAAAAAAAAAA-1,autocrine,high,96.77,unreviewed +GTAGCAAAAAAAAAAA-1|TTAGCAAAAAAAAAAA-1,paracrine,mid,64.07,unreviewed +GTATAAAAAAAAAAAA-1|CTATAAAAAAAAAAAA-1,paracrine,mid,54.66,reviewed +GTATAAAAAAAAAAAA-1|GATGAAAAAAAAAAAA-1,paracrine,low,17.66,unreviewed +GTATAAAAAAAAAAAA-1|GGGTAAAAAAAAAAAA-1,paracrine,mid,34.47,unreviewed +GTATAAAAAAAAAAAA-1|GTATAAAAAAAAAAAA-1,autocrine,low,25.15,reviewed +GTATAAAAAAAAAAAA-1|TATGAAAAAAAAAAAA-1,paracrine,low,10.84,unreviewed +GTATAAAAAAAAAAAA-1|TGGTAAAAAAAAAAAA-1,paracrine,low,6.09,reviewed +GTATAAAAAAAAAAAA-1|TTATAAAAAAAAAAAA-1,paracrine,low,7.73,reviewed +GTATCAAAAAAAAAAA-1|CATGCAAAAAAAAAAA-1,paracrine,high,91.17,unreviewed +GTATCAAAAAAAAAAA-1|CGGTCAAAAAAAAAAA-1,paracrine,high,83.28,reviewed +GTATCAAAAAAAAAAA-1|CTATCAAAAAAAAAAA-1,paracrine,high,88.92,unreviewed +GTATCAAAAAAAAAAA-1|GATGCAAAAAAAAAAA-1,paracrine,high,97.02,unreviewed +GTATCAAAAAAAAAAA-1|GGGTCAAAAAAAAAAA-1,paracrine,high,69.34,unreviewed +GTATCAAAAAAAAAAA-1|GTATCAAAAAAAAAAA-1,autocrine,high,83.28,reviewed +GTATCAAAAAAAAAAA-1|TTATCAAAAAAAAAAA-1,paracrine,mid,53.41,unreviewed +GTCACAAAAAAAAAAA-1|CAAACAAAAAAAAAAA-1,paracrine,low,10.57,unreviewed +GTCACAAAAAAAAAAA-1|CGTACAAAAAAAAAAA-1,paracrine,low,9.32,unreviewed +GTCACAAAAAAAAAAA-1|CTCACAAAAAAAAAAA-1,paracrine,low,6.33,unreviewed +GTCACAAAAAAAAAAA-1|GAAACAAAAAAAAAAA-1,paracrine,low,6.52,unreviewed +GTCACAAAAAAAAAAA-1|GGTACAAAAAAAAAAA-1,paracrine,low,9.44,unreviewed +GTCACAAAAAAAAAAA-1|GTCACAAAAAAAAAAA-1,autocrine,low,5.66,reviewed +GTCACAAAAAAAAAAA-1|TTCACAAAAAAAAAAA-1,paracrine,low,13.67,unreviewed +GTCCCAAAAAAAAAAA-1|CAACCAAAAAAAAAAA-1,paracrine,low,32.55,reviewed +GTCCCAAAAAAAAAAA-1|CGTCCAAAAAAAAAAA-1,paracrine,high,71.19,unreviewed +GTCCCAAAAAAAAAAA-1|CTCCCAAAAAAAAAAA-1,paracrine,low,29.54,reviewed +GTCCCAAAAAAAAAAA-1|GAACCAAAAAAAAAAA-1,paracrine,low,19.49,reviewed +GTCCCAAAAAAAAAAA-1|GGTCCAAAAAAAAAAA-1,paracrine,mid,55.85,unreviewed +GTCCCAAAAAAAAAAA-1|GTCCCAAAAAAAAAAA-1,autocrine,mid,49.09,unreviewed +GTCCCAAAAAAAAAAA-1|TTCCCAAAAAAAAAAA-1,paracrine,high,78.08,unreviewed +GTCGAAAAAAAAAAAA-1|CTCGAAAAAAAAAAAA-1,paracrine,mid,52.28,unreviewed +GTCGAAAAAAAAAAAA-1|GAAGAAAAAAAAAAAA-1,paracrine,mid,33.4,reviewed +GTCGAAAAAAAAAAAA-1|GGTGAAAAAAAAAAAA-1,paracrine,mid,50.58,unreviewed +GTCGAAAAAAAAAAAA-1|GTCGAAAAAAAAAAAA-1,autocrine,mid,44.43,unreviewed +GTCGAAAAAAAAAAAA-1|TAAGAAAAAAAAAAAA-1,paracrine,low,12.39,reviewed +GTCGAAAAAAAAAAAA-1|TGTGAAAAAAAAAAAA-1,paracrine,low,26.16,unreviewed +GTCGAAAAAAAAAAAA-1|TTCGAAAAAAAAAAAA-1,paracrine,high,79.45,unreviewed +GTCGCAAAAAAAAAAA-1|CAAGCAAAAAAAAAAA-1,paracrine,high,94.0,reviewed +GTCGCAAAAAAAAAAA-1|CGTGCAAAAAAAAAAA-1,paracrine,mid,49.3,unreviewed +GTCGCAAAAAAAAAAA-1|CTCGCAAAAAAAAAAA-1,paracrine,high,74.94,unreviewed +GTCGCAAAAAAAAAAA-1|GAAGCAAAAAAAAAAA-1,paracrine,mid,50.58,unreviewed +GTCGCAAAAAAAAAAA-1|GTCGCAAAAAAAAAAA-1,autocrine,high,90.65,unreviewed +GTGACAAAAAAAAAAA-1|CACACAAAAAAAAAAA-1,paracrine,high,91.35,reviewed +GTGACAAAAAAAAAAA-1|CGACCAAAAAAAAAAA-1,paracrine,mid,41.99,unreviewed +GTGACAAAAAAAAAAA-1|CTGACAAAAAAAAAAA-1,paracrine,high,83.19,unreviewed +GTGACAAAAAAAAAAA-1|GACACAAAAAAAAAAA-1,paracrine,mid,65.83,unreviewed +GTGACAAAAAAAAAAA-1|GGACCAAAAAAAAAAA-1,paracrine,high,91.05,reviewed +GTGACAAAAAAAAAAA-1|GTGACAAAAAAAAAAA-1,autocrine,mid,52.62,reviewed +GTGACAAAAAAAAAAA-1|TTGACAAAAAAAAAAA-1,paracrine,mid,44.12,reviewed +GTGGAAAAAAAAAAAA-1|CGATAAAAAAAAAAAA-1,paracrine,mid,40.32,unreviewed +GTGGAAAAAAAAAAAA-1|GGATAAAAAAAAAAAA-1,paracrine,low,26.95,unreviewed +GTGGAAAAAAAAAAAA-1|GTGGAAAAAAAAAAAA-1,autocrine,low,26.83,unreviewed +GTGGAAAAAAAAAAAA-1|TTGGAAAAAAAAAAAA-1,paracrine,low,16.38,reviewed +GTGGCAAAAAAAAAAA-1|CTGGCAAAAAAAAAAA-1,paracrine,high,93.03,reviewed +GTGGCAAAAAAAAAAA-1|GACGCAAAAAAAAAAA-1,paracrine,high,76.28,unreviewed +GTGGCAAAAAAAAAAA-1|GGATCAAAAAAAAAAA-1,paracrine,high,87.09,reviewed +GTGGCAAAAAAAAAAA-1|GTGGCAAAAAAAAAAA-1,autocrine,high,73.39,unreviewed +GTGGCAAAAAAAAAAA-1|TACGCAAAAAAAAAAA-1,paracrine,low,8.5,unreviewed +GTGGCAAAAAAAAAAA-1|TGATCAAAAAAAAAAA-1,paracrine,low,10.26,unreviewed +GTGGCAAAAAAAAAAA-1|TTGGCAAAAAAAAAAA-1,paracrine,low,7.0,unreviewed +GTGTAAAAAAAAAAAA-1|CACTAAAAAAAAAAAA-1,paracrine,low,9.5,reviewed +GTGTAAAAAAAAAAAA-1|CGAACAAAAAAAAAAA-1,paracrine,low,18.64,unreviewed +GTGTAAAAAAAAAAAA-1|CTGTAAAAAAAAAAAA-1,paracrine,mid,39.59,unreviewed +GTGTAAAAAAAAAAAA-1|GACTAAAAAAAAAAAA-1,paracrine,mid,39.22,unreviewed +GTGTAAAAAAAAAAAA-1|GGAACAAAAAAAAAAA-1,paracrine,low,14.77,unreviewed +GTGTAAAAAAAAAAAA-1|GTGTAAAAAAAAAAAA-1,autocrine,low,24.94,unreviewed +GTGTAAAAAAAAAAAA-1|TTGTAAAAAAAAAAAA-1,paracrine,low,20.04,unreviewed +GTGTCAAAAAAAAAAA-1|CTGTCAAAAAAAAAAA-1,paracrine,high,79.87,unreviewed +GTGTCAAAAAAAAAAA-1|GACTCAAAAAAAAAAA-1,paracrine,high,96.77,unreviewed +GTGTCAAAAAAAAAAA-1|GTGTCAAAAAAAAAAA-1,autocrine,high,85.99,unreviewed +GTGTCAAAAAAAAAAA-1|TACTCAAAAAAAAAAA-1,paracrine,high,85.81,unreviewed +GTGTCAAAAAAAAAAA-1|TTGTCAAAAAAAAAAA-1,paracrine,high,72.08,reviewed +GTTACAAAAAAAAAAA-1|CTTACAAAAAAAAAAA-1,paracrine,mid,57.92,unreviewed +GTTACAAAAAAAAAAA-1|GAGACAAAAAAAAAAA-1,paracrine,mid,35.99,unreviewed +GTTACAAAAAAAAAAA-1|GGCCCAAAAAAAAAAA-1,paracrine,high,90.74,unreviewed +GTTACAAAAAAAAAAA-1|GTTACAAAAAAAAAAA-1,autocrine,high,77.34,unreviewed +GTTACAAAAAAAAAAA-1|TAGACAAAAAAAAAAA-1,paracrine,low,8.68,unreviewed +GTTACAAAAAAAAAAA-1|TGCCCAAAAAAAAAAA-1,paracrine,low,6.03,unreviewed +GTTACAAAAAAAAAAA-1|TTTACAAAAAAAAAAA-1,paracrine,low,16.87,reviewed +GTTCAAAAAAAAAAAA-1|CAGCAAAAAAAAAAAA-1,paracrine,low,26.49,unreviewed +GTTCAAAAAAAAAAAA-1|CGCGAAAAAAAAAAAA-1,paracrine,high,73.11,reviewed +GTTCAAAAAAAAAAAA-1|CTTCAAAAAAAAAAAA-1,paracrine,mid,41.23,unreviewed +GTTCAAAAAAAAAAAA-1|GAGCAAAAAAAAAAAA-1,paracrine,mid,35.84,unreviewed +GTTCAAAAAAAAAAAA-1|GGCGAAAAAAAAAAAA-1,paracrine,low,16.75,reviewed +GTTCAAAAAAAAAAAA-1|GTTCAAAAAAAAAAAA-1,autocrine,low,27.47,reviewed +GTTCAAAAAAAAAAAA-1|TTTCAAAAAAAAAAAA-1,paracrine,low,33.04,reviewed +GTTCCAAAAAAAAAAA-1|CTTCCAAAAAAAAAAA-1,paracrine,mid,43.33,unreviewed +GTTCCAAAAAAAAAAA-1|GAGCCAAAAAAAAAAA-1,paracrine,low,32.67,unreviewed +GTTCCAAAAAAAAAAA-1|GGCGCAAAAAAAAAAA-1,paracrine,low,10.17,reviewed +GTTCCAAAAAAAAAAA-1|GTTCCAAAAAAAAAAA-1,autocrine,low,10.96,unreviewed +GTTCCAAAAAAAAAAA-1|TAGCCAAAAAAAAAAA-1,paracrine,low,20.58,unreviewed +GTTCCAAAAAAAAAAA-1|TGCGCAAAAAAAAAAA-1,paracrine,low,20.77,unreviewed +GTTCCAAAAAAAAAAA-1|TTTCCAAAAAAAAAAA-1,paracrine,low,8.8,unreviewed +TAAACAAAAAAAAAAA-1|AAGACAAAAAAAAAAA-1,paracrine,mid,42.54,reviewed +TAAACAAAAAAAAAAA-1|ACAACAAAAAAAAAAA-1,paracrine,high,71.68,unreviewed +TAAACAAAAAAAAAAA-1|AGGTAAAAAAAAAAAA-1,paracrine,mid,55.24,reviewed +TAAACAAAAAAAAAAA-1|GAAACAAAAAAAAAAA-1,paracrine,mid,41.23,unreviewed +TAAACAAAAAAAAAAA-1|TAAACAAAAAAAAAAA-1,autocrine,mid,51.28,unreviewed +TAAACAAAAAAAAAAA-1|TCGTAAAAAAAAAAAA-1,paracrine,high,67.05,reviewed +TAAACAAAAAAAAAAA-1|TTCACAAAAAAAAAAA-1,paracrine,high,85.9,reviewed +TAAAGAAAAAAAAAAA-1|ACAAGAAAAAAAAAAA-1,paracrine,mid,35.57,unreviewed +TAAAGAAAAAAAAAAA-1|GCGTCAAAAAAAAAAA-1,paracrine,low,0.97,unreviewed +TAAAGAAAAAAAAAAA-1|TAAAGAAAAAAAAAAA-1,autocrine,high,75.33,reviewed +TAAAGAAAAAAAAAAA-1|TCGTCAAAAAAAAAAA-1,paracrine,high,72.32,unreviewed +TAACCAAAAAAAAAAA-1|AAGCCAAAAAAAAAAA-1,paracrine,high,78.29,unreviewed +TAACCAAAAAAAAAAA-1|ACACCAAAAAAAAAAA-1,paracrine,low,2.56,reviewed +TAACCAAAAAAAAAAA-1|AGGACAAAAAAAAAAA-1,paracrine,high,83.68,unreviewed +TAACCAAAAAAAAAAA-1|GAACCAAAAAAAAAAA-1,paracrine,mid,61.21,reviewed +TAACCAAAAAAAAAAA-1|TAACCAAAAAAAAAAA-1,autocrine,mid,65.07,reviewed +TAACCAAAAAAAAAAA-1|TCGACAAAAAAAAAAA-1,paracrine,high,88.25,unreviewed +TAACCAAAAAAAAAAA-1|TTCCCAAAAAAAAAAA-1,paracrine,high,98.17,unreviewed +TAAGAAAAAAAAAAAA-1|GAAGAAAAAAAAAAAA-1,paracrine,mid,45.01,unreviewed +TAAGAAAAAAAAAAAA-1|GTCGAAAAAAAAAAAA-1,paracrine,mid,55.3,reviewed +TAAGAAAAAAAAAAAA-1|TAAGAAAAAAAAAAAA-1,autocrine,low,17.75,unreviewed +TAAGAAAAAAAAAAAA-1|TTCGAAAAAAAAAAAA-1,paracrine,high,84.71,unreviewed +TAAGCAAAAAAAAAAA-1|GAAGCAAAAAAAAAAA-1,paracrine,mid,45.19,reviewed +TAAGCAAAAAAAAAAA-1|TAAGCAAAAAAAAAAA-1,autocrine,low,24.7,reviewed +TAAGCAAAAAAAAAAA-1|TCGCCAAAAAAAAAAA-1,paracrine,low,26.16,unreviewed +TACACAAAAAAAAAAA-1|GACACAAAAAAAAAAA-1,paracrine,mid,48.23,reviewed +TACACAAAAAAAAAAA-1|TACACAAAAAAAAAAA-1,autocrine,mid,37.61,unreviewed +TACACAAAAAAAAAAA-1|TCTTAAAAAAAAAAAA-1,paracrine,mid,37.7,reviewed +TACACAAAAAAAAAAA-1|TTGACAAAAAAAAAAA-1,paracrine,low,25.88,unreviewed +TACGAAAAAAAAAAAA-1|AATGAAAAAAAAAAAA-1,paracrine,mid,51.89,unreviewed +TACGAAAAAAAAAAAA-1|ACCGAAAAAAAAAAAA-1,paracrine,high,70.58,unreviewed +TACGAAAAAAAAAAAA-1|AGTCAAAAAAAAAAAA-1,paracrine,mid,60.66,reviewed +TACGAAAAAAAAAAAA-1|TACGAAAAAAAAAAAA-1,autocrine,low,29.6,unreviewed +TACGAAAAAAAAAAAA-1|TTGGAAAAAAAAAAAA-1,paracrine,mid,43.88,reviewed +TACGCAAAAAAAAAAA-1|ACCGCAAAAAAAAAAA-1,paracrine,high,72.96,reviewed +TACGCAAAAAAAAAAA-1|GACGCAAAAAAAAAAA-1,paracrine,low,0.12,unreviewed +TACGCAAAAAAAAAAA-1|GCTCCAAAAAAAAAAA-1,paracrine,low,0.43,unreviewed +TACGCAAAAAAAAAAA-1|GTGGCAAAAAAAAAAA-1,paracrine,low,0.06,unreviewed +TACGCAAAAAAAAAAA-1|TACGCAAAAAAAAAAA-1,autocrine,high,80.88,unreviewed +TACGCAAAAAAAAAAA-1|TCTCCAAAAAAAAAAA-1,paracrine,mid,63.89,unreviewed +TACGCAAAAAAAAAAA-1|TTGGCAAAAAAAAAAA-1,paracrine,high,92.87,unreviewed +TACTAAAAAAAAAAAA-1|AATTAAAAAAAAAAAA-1,paracrine,mid,50.37,reviewed +TACTAAAAAAAAAAAA-1|ACCTAAAAAAAAAAAA-1,paracrine,low,4.69,reviewed +TACTAAAAAAAAAAAA-1|AGTGAAAAAAAAAAAA-1,paracrine,high,68.51,reviewed +TACTAAAAAAAAAAAA-1|GACTAAAAAAAAAAAA-1,paracrine,high,73.23,reviewed +TACTAAAAAAAAAAAA-1|TACTAAAAAAAAAAAA-1,autocrine,low,30.36,unreviewed +TACTAAAAAAAAAAAA-1|TCTGAAAAAAAAAAAA-1,paracrine,mid,59.81,unreviewed +TACTAAAAAAAAAAAA-1|TTGTAAAAAAAAAAAA-1,paracrine,mid,45.83,reviewed +TACTCAAAAAAAAAAA-1|ACCTCAAAAAAAAAAA-1,paracrine,high,90.41,reviewed +TACTCAAAAAAAAAAA-1|GACTCAAAAAAAAAAA-1,paracrine,high,95.22,unreviewed +TACTCAAAAAAAAAAA-1|GCTGCAAAAAAAAAAA-1,paracrine,high,94.52,reviewed +TACTCAAAAAAAAAAA-1|GTGTCAAAAAAAAAAA-1,paracrine,high,82.76,unreviewed +TACTCAAAAAAAAAAA-1|TACTCAAAAAAAAAAA-1,autocrine,high,84.59,unreviewed +TACTCAAAAAAAAAAA-1|TCTGCAAAAAAAAAAA-1,paracrine,high,87.7,unreviewed +TACTCAAAAAAAAAAA-1|TTGTCAAAAAAAAAAA-1,paracrine,high,73.39,unreviewed +TAGACAAAAAAAAAAA-1|ACGACAAAAAAAAAAA-1,paracrine,high,88.37,reviewed +TAGACAAAAAAAAAAA-1|GAGACAAAAAAAAAAA-1,paracrine,low,0.91,unreviewed +TAGACAAAAAAAAAAA-1|GCAACAAAAAAAAAAA-1,paracrine,low,1.83,unreviewed +TAGACAAAAAAAAAAA-1|GTTACAAAAAAAAAAA-1,paracrine,low,1.71,unreviewed +TAGACAAAAAAAAAAA-1|TAGACAAAAAAAAAAA-1,autocrine,high,91.93,unreviewed +TAGACAAAAAAAAAAA-1|TCAACAAAAAAAAAAA-1,paracrine,high,99.21,unreviewed +TAGACAAAAAAAAAAA-1|TTTACAAAAAAAAAAA-1,paracrine,high,91.11,unreviewed +TAGCAAAAAAAAAAAA-1|AAAGAAAAAAAAAAAA-1,paracrine,high,76.28,reviewed +TAGCAAAAAAAAAAAA-1|ACGCAAAAAAAAAAAA-1,paracrine,low,3.53,unreviewed +TAGCAAAAAAAAAAAA-1|GAGCAAAAAAAAAAAA-1,paracrine,mid,35.99,unreviewed +TAGCAAAAAAAAAAAA-1|TAGCAAAAAAAAAAAA-1,autocrine,low,32.55,unreviewed +TAGCAAAAAAAAAAAA-1|TTTCAAAAAAAAAAAA-1,paracrine,mid,34.38,unreviewed +TAGCCAAAAAAAAAAA-1|ACGCCAAAAAAAAAAA-1,paracrine,low,19.67,unreviewed +TAGCCAAAAAAAAAAA-1|GAGCCAAAAAAAAAAA-1,paracrine,mid,48.02,reviewed +TAGCCAAAAAAAAAAA-1|GCACCAAAAAAAAAAA-1,paracrine,mid,46.13,unreviewed +TAGCCAAAAAAAAAAA-1|GTTCCAAAAAAAAAAA-1,paracrine,low,21.01,unreviewed +TAGCCAAAAAAAAAAA-1|TAGCCAAAAAAAAAAA-1,autocrine,mid,47.26,unreviewed +TAGCCAAAAAAAAAAA-1|TCACCAAAAAAAAAAA-1,paracrine,mid,46.44,reviewed +TAGCCAAAAAAAAAAA-1|TTTCCAAAAAAAAAAA-1,paracrine,low,20.89,unreviewed +TATCCAAAAAAAAAAA-1|AACGCAAAAAAAAAAA-1,paracrine,low,30.6,unreviewed +TATCCAAAAAAAAAAA-1|ACTCCAAAAAAAAAAA-1,paracrine,mid,49.67,unreviewed +TATCCAAAAAAAAAAA-1|AGCCCAAAAAAAAAAA-1,paracrine,mid,56.52,reviewed +TATCCAAAAAAAAAAA-1|GATCCAAAAAAAAAAA-1,paracrine,high,67.11,reviewed +TATCCAAAAAAAAAAA-1|TATCCAAAAAAAAAAA-1,autocrine,high,68.64,unreviewed +TATCCAAAAAAAAAAA-1|TCCCCAAAAAAAAAAA-1,paracrine,low,13.95,unreviewed +TATCCAAAAAAAAAAA-1|TTAGCAAAAAAAAAAA-1,paracrine,low,26.67,unreviewed +TATGAAAAAAAAAAAA-1|ACTGAAAAAAAAAAAA-1,paracrine,high,74.33,unreviewed +TATGAAAAAAAAAAAA-1|GATGAAAAAAAAAAAA-1,paracrine,low,0.79,unreviewed +TATGAAAAAAAAAAAA-1|GCCGAAAAAAAAAAAA-1,paracrine,low,0.67,unreviewed +TATGAAAAAAAAAAAA-1|GTATAAAAAAAAAAAA-1,paracrine,low,0.3,reviewed +TATGAAAAAAAAAAAA-1|TATGAAAAAAAAAAAA-1,autocrine,high,80.45,unreviewed +TATGAAAAAAAAAAAA-1|TCCGAAAAAAAAAAAA-1,paracrine,mid,55.39,reviewed +TATGAAAAAAAAAAAA-1|TTATAAAAAAAAAAAA-1,paracrine,mid,45.62,unreviewed +TATGCAAAAAAAAAAA-1|AACTCAAAAAAAAAAA-1,paracrine,low,25.06,reviewed +TATGCAAAAAAAAAAA-1|ACTGCAAAAAAAAAAA-1,paracrine,low,2.5,unreviewed +TATGCAAAAAAAAAAA-1|AGCGCAAAAAAAAAAA-1,paracrine,low,11.02,unreviewed +TATGCAAAAAAAAAAA-1|GATGCAAAAAAAAAAA-1,paracrine,high,79.35,unreviewed +TATGCAAAAAAAAAAA-1|TATGCAAAAAAAAAAA-1,autocrine,high,80.15,unreviewed +TATGCAAAAAAAAAAA-1|TCCGCAAAAAAAAAAA-1,paracrine,mid,53.81,unreviewed +TATGCAAAAAAAAAAA-1|TTATCAAAAAAAAAAA-1,paracrine,low,25.49,unreviewed +TATTAAAAAAAAAAAA-1|ACTTAAAAAAAAAAAA-1,paracrine,mid,36.24,reviewed +TATTAAAAAAAAAAAA-1|GATTAAAAAAAAAAAA-1,paracrine,mid,63.37,unreviewed +TATTAAAAAAAAAAAA-1|GCCTAAAAAAAAAAAA-1,paracrine,high,81.43,reviewed +TATTAAAAAAAAAAAA-1|GTAACAAAAAAAAAAA-1,paracrine,mid,51.74,unreviewed +TATTAAAAAAAAAAAA-1|TATTAAAAAAAAAAAA-1,autocrine,high,87.76,reviewed +TATTAAAAAAAAAAAA-1|TCCTAAAAAAAAAAAA-1,paracrine,mid,64.65,unreviewed +TATTAAAAAAAAAAAA-1|TTAACAAAAAAAAAAA-1,paracrine,low,30.48,reviewed +TCAACAAAAAAAAAAA-1|ACGACAAAAAAAAAAA-1,paracrine,low,27.62,reviewed +TCAACAAAAAAAAAAA-1|AGAACAAAAAAAAAAA-1,paracrine,mid,38.95,unreviewed +TCAACAAAAAAAAAAA-1|ATGTAAAAAAAAAAAA-1,paracrine,mid,49.0,unreviewed +TCAACAAAAAAAAAAA-1|GCAACAAAAAAAAAAA-1,paracrine,low,0.73,unreviewed +TCAACAAAAAAAAAAA-1|TAGACAAAAAAAAAAA-1,paracrine,mid,37.0,unreviewed +TCAACAAAAAAAAAAA-1|TCAACAAAAAAAAAAA-1,autocrine,mid,55.51,unreviewed +TCAACAAAAAAAAAAA-1|TGGTAAAAAAAAAAAA-1,paracrine,low,16.38,reviewed +TCAAGAAAAAAAAAAA-1|AGAAGAAAAAAAAAAA-1,paracrine,high,67.17,unreviewed +TCAAGAAAAAAAAAAA-1|GCAAGAAAAAAAAAAA-1,paracrine,high,80.82,reviewed +TCAAGAAAAAAAAAAA-1|GGGTCAAAAAAAAAAA-1,paracrine,high,88.98,unreviewed +TCAAGAAAAAAAAAAA-1|TCAAGAAAAAAAAAAA-1,autocrine,high,70.16,reviewed +TCAAGAAAAAAAAAAA-1|TGGTCAAAAAAAAAAA-1,paracrine,low,19.06,unreviewed +TCACCAAAAAAAAAAA-1|ACGCCAAAAAAAAAAA-1,paracrine,low,24.0,reviewed +TCACCAAAAAAAAAAA-1|AGACCAAAAAAAAAAA-1,paracrine,mid,54.42,unreviewed +TCACCAAAAAAAAAAA-1|ATGACAAAAAAAAAAA-1,paracrine,mid,63.58,reviewed +TCACCAAAAAAAAAAA-1|GCACCAAAAAAAAAAA-1,paracrine,mid,65.07,reviewed +TCACCAAAAAAAAAAA-1|TAGCCAAAAAAAAAAA-1,paracrine,high,71.19,unreviewed +TCACCAAAAAAAAAAA-1|TCACCAAAAAAAAAAA-1,autocrine,mid,49.67,unreviewed +TCACCAAAAAAAAAAA-1|TGGACAAAAAAAAAAA-1,paracrine,low,17.11,reviewed +TCATCAAAAAAAAAAA-1|AGATCAAAAAAAAAAA-1,paracrine,low,11.75,reviewed +TCATCAAAAAAAAAAA-1|GGGGCAAAAAAAAAAA-1,paracrine,low,12.61,reviewed +TCATCAAAAAAAAAAA-1|TCATCAAAAAAAAAAA-1,autocrine,low,13.31,reviewed +TCATCAAAAAAAAAAA-1|TGGGCAAAAAAAAAAA-1,paracrine,low,21.41,reviewed +TCCCCAAAAAAAAAAA-1|AGCCCAAAAAAAAAAA-1,paracrine,high,69.15,unreviewed +TCCCCAAAAAAAAAAA-1|GATCCAAAAAAAAAAA-1,paracrine,high,81.73,unreviewed +TCCCCAAAAAAAAAAA-1|GCCCCAAAAAAAAAAA-1,paracrine,high,67.87,reviewed +TCCCCAAAAAAAAAAA-1|GGTACAAAAAAAAAAA-1,paracrine,high,71.56,unreviewed +TCCCCAAAAAAAAAAA-1|TATCCAAAAAAAAAAA-1,paracrine,high,79.29,unreviewed +TCCCCAAAAAAAAAAA-1|TCCCCAAAAAAAAAAA-1,autocrine,low,19.85,reviewed +TCCCCAAAAAAAAAAA-1|TGTACAAAAAAAAAAA-1,paracrine,low,29.08,reviewed +TCCGAAAAAAAAAAAA-1|ACTGAAAAAAAAAAAA-1,paracrine,mid,43.45,unreviewed +TCCGAAAAAAAAAAAA-1|AGCGAAAAAAAAAAAA-1,paracrine,mid,62.7,unreviewed +TCCGAAAAAAAAAAAA-1|ATTCAAAAAAAAAAAA-1,paracrine,mid,40.99,unreviewed +TCCGAAAAAAAAAAAA-1|GCCGAAAAAAAAAAAA-1,paracrine,low,0.55,unreviewed +TCCGAAAAAAAAAAAA-1|TATGAAAAAAAAAAAA-1,paracrine,mid,52.44,reviewed +TCCGAAAAAAAAAAAA-1|TCCGAAAAAAAAAAAA-1,autocrine,low,26.16,unreviewed +TCCGAAAAAAAAAAAA-1|TGTCAAAAAAAAAAAA-1,paracrine,mid,65.74,reviewed +TCCGCAAAAAAAAAAA-1|AGCGCAAAAAAAAAAA-1,paracrine,low,19.61,unreviewed +TCCGCAAAAAAAAAAA-1|GATGCAAAAAAAAAAA-1,paracrine,high,94.4,unreviewed +TCCGCAAAAAAAAAAA-1|GCCGCAAAAAAAAAAA-1,paracrine,high,85.32,unreviewed +TCCGCAAAAAAAAAAA-1|GGTCCAAAAAAAAAAA-1,paracrine,high,81.85,unreviewed +TCCGCAAAAAAAAAAA-1|TATGCAAAAAAAAAAA-1,paracrine,high,92.81,reviewed +TCCGCAAAAAAAAAAA-1|TCCGCAAAAAAAAAAA-1,autocrine,high,76.77,unreviewed +TCCGCAAAAAAAAAAA-1|TGTCCAAAAAAAAAAA-1,paracrine,mid,49.0,unreviewed +TCCTAAAAAAAAAAAA-1|ACTTAAAAAAAAAAAA-1,paracrine,mid,60.81,unreviewed +TCCTAAAAAAAAAAAA-1|AGCTAAAAAAAAAAAA-1,paracrine,mid,56.88,unreviewed +TCCTAAAAAAAAAAAA-1|ATTGAAAAAAAAAAAA-1,paracrine,high,93.42,unreviewed +TCCTAAAAAAAAAAAA-1|GCCTAAAAAAAAAAAA-1,paracrine,high,93.03,reviewed +TCCTAAAAAAAAAAAA-1|TATTAAAAAAAAAAAA-1,paracrine,high,96.89,unreviewed +TCCTAAAAAAAAAAAA-1|TCCTAAAAAAAAAAAA-1,autocrine,high,78.14,unreviewed +TCCTAAAAAAAAAAAA-1|TGTGAAAAAAAAAAAA-1,paracrine,high,69.82,reviewed +TCGACAAAAAAAAAAA-1|AGGACAAAAAAAAAAA-1,paracrine,high,78.81,unreviewed +TCGACAAAAAAAAAAA-1|GAACCAAAAAAAAAAA-1,paracrine,mid,55.39,reviewed +TCGACAAAAAAAAAAA-1|GCGACAAAAAAAAAAA-1,paracrine,mid,60.2,unreviewed +TCGACAAAAAAAAAAA-1|GGAACAAAAAAAAAAA-1,paracrine,mid,62.85,reviewed +TCGACAAAAAAAAAAA-1|TAACCAAAAAAAAAAA-1,paracrine,mid,57.43,unreviewed +TCGACAAAAAAAAAAA-1|TCGACAAAAAAAAAAA-1,autocrine,high,85.2,reviewed +TCGACAAAAAAAAAAA-1|TGAACAAAAAAAAAAA-1,paracrine,high,85.75,reviewed +TCGCCAAAAAAAAAAA-1|GAAGCAAAAAAAAAAA-1,paracrine,high,74.94,unreviewed +TCGCCAAAAAAAAAAA-1|GCGCCAAAAAAAAAAA-1,paracrine,high,92.2,reviewed +TCGCCAAAAAAAAAAA-1|GGACCAAAAAAAAAAA-1,paracrine,high,98.54,reviewed +TCGCCAAAAAAAAAAA-1|TAAGCAAAAAAAAAAA-1,paracrine,mid,61.33,unreviewed +TCGCCAAAAAAAAAAA-1|TCGCCAAAAAAAAAAA-1,autocrine,mid,49.3,reviewed +TCGCCAAAAAAAAAAA-1|TGACCAAAAAAAAAAA-1,paracrine,mid,46.65,unreviewed +TCGTAAAAAAAAAAAA-1|AGGTAAAAAAAAAAAA-1,paracrine,mid,50.18,unreviewed +TCGTAAAAAAAAAAAA-1|GAAACAAAAAAAAAAA-1,paracrine,low,30.91,unreviewed +TCGTAAAAAAAAAAAA-1|GCGTAAAAAAAAAAAA-1,paracrine,mid,34.56,unreviewed +TCGTAAAAAAAAAAAA-1|GGATAAAAAAAAAAAA-1,paracrine,mid,60.72,unreviewed +TCGTAAAAAAAAAAAA-1|TAAACAAAAAAAAAAA-1,paracrine,high,69.61,unreviewed +TCGTAAAAAAAAAAAA-1|TCGTAAAAAAAAAAAA-1,autocrine,high,77.83,reviewed +TCGTAAAAAAAAAAAA-1|TGATAAAAAAAAAAAA-1,paracrine,mid,56.21,reviewed +TCGTCAAAAAAAAAAA-1|ACAAGAAAAAAAAAAA-1,paracrine,low,21.86,unreviewed +TCGTCAAAAAAAAAAA-1|AGGTCAAAAAAAAAAA-1,paracrine,mid,58.68,unreviewed +TCGTCAAAAAAAAAAA-1|ATATCAAAAAAAAAAA-1,paracrine,mid,45.83,unreviewed +TCGTCAAAAAAAAAAA-1|GCGTCAAAAAAAAAAA-1,paracrine,low,0.61,reviewed +TCGTCAAAAAAAAAAA-1|TAAAGAAAAAAAAAAA-1,paracrine,mid,59.1,reviewed +TCGTCAAAAAAAAAAA-1|TCGTCAAAAAAAAAAA-1,autocrine,mid,53.23,unreviewed +TCGTCAAAAAAAAAAA-1|TGATCAAAAAAAAAAA-1,paracrine,high,80.72,unreviewed +TCTCCAAAAAAAAAAA-1|ACCGCAAAAAAAAAAA-1,paracrine,mid,44.28,unreviewed +TCTCCAAAAAAAAAAA-1|AGTCCAAAAAAAAAAA-1,paracrine,mid,61.94,reviewed +TCTCCAAAAAAAAAAA-1|ATCCCAAAAAAAAAAA-1,paracrine,high,86.91,unreviewed +TCTCCAAAAAAAAAAA-1|GCTCCAAAAAAAAAAA-1,paracrine,low,1.22,unreviewed +TCTCCAAAAAAAAAAA-1|TACGCAAAAAAAAAAA-1,paracrine,mid,56.52,unreviewed +TCTCCAAAAAAAAAAA-1|TCTCCAAAAAAAAAAA-1,autocrine,mid,39.4,reviewed +TCTCCAAAAAAAAAAA-1|TGCCCAAAAAAAAAAA-1,paracrine,mid,59.93,unreviewed +TCTGAAAAAAAAAAAA-1|AGTGAAAAAAAAAAAA-1,paracrine,high,95.55,unreviewed +TCTGAAAAAAAAAAAA-1|GACTAAAAAAAAAAAA-1,paracrine,high,96.95,unreviewed +TCTGAAAAAAAAAAAA-1|GCTGAAAAAAAAAAAA-1,paracrine,high,95.89,unreviewed +TCTGAAAAAAAAAAAA-1|GGCGAAAAAAAAAAAA-1,paracrine,mid,54.2,unreviewed +TCTGAAAAAAAAAAAA-1|TACTAAAAAAAAAAAA-1,paracrine,high,71.86,unreviewed +TCTGAAAAAAAAAAAA-1|TCTGAAAAAAAAAAAA-1,autocrine,high,90.56,unreviewed +TCTGAAAAAAAAAAAA-1|TGCGAAAAAAAAAAAA-1,paracrine,high,97.69,unreviewed +TCTGCAAAAAAAAAAA-1|ACCTCAAAAAAAAAAA-1,paracrine,high,98.48,reviewed +TCTGCAAAAAAAAAAA-1|AGTGCAAAAAAAAAAA-1,paracrine,mid,63.37,reviewed +TCTGCAAAAAAAAAAA-1|ATCGCAAAAAAAAAAA-1,paracrine,high,97.56,unreviewed +TCTGCAAAAAAAAAAA-1|GCTGCAAAAAAAAAAA-1,paracrine,high,98.9,reviewed +TCTGCAAAAAAAAAAA-1|TACTCAAAAAAAAAAA-1,paracrine,high,96.65,unreviewed +TCTGCAAAAAAAAAAA-1|TCTGCAAAAAAAAAAA-1,autocrine,high,97.99,unreviewed +TCTGCAAAAAAAAAAA-1|TGCGCAAAAAAAAAAA-1,paracrine,high,93.7,reviewed +TCTTAAAAAAAAAAAA-1|GACACAAAAAAAAAAA-1,paracrine,high,85.51,reviewed +TCTTAAAAAAAAAAAA-1|GCTTAAAAAAAAAAAA-1,paracrine,high,95.8,unreviewed +TCTTAAAAAAAAAAAA-1|GGCTAAAAAAAAAAAA-1,paracrine,high,71.32,unreviewed +TCTTAAAAAAAAAAAA-1|TACACAAAAAAAAAAA-1,paracrine,high,75.73,reviewed +TCTTAAAAAAAAAAAA-1|TCTTAAAAAAAAAAAA-1,autocrine,high,70.92,reviewed +TGAACAAAAAAAAAAA-1|AATTAAAAAAAAAAAA-1,paracrine,high,78.2,unreviewed +TGAACAAAAAAAAAAA-1|AGGACAAAAAAAAAAA-1,paracrine,high,81.3,unreviewed +TGAACAAAAAAAAAAA-1|ATAACAAAAAAAAAAA-1,paracrine,low,2.04,unreviewed +TGAACAAAAAAAAAAA-1|GGAACAAAAAAAAAAA-1,paracrine,high,72.59,reviewed +TGAACAAAAAAAAAAA-1|TCGACAAAAAAAAAAA-1,paracrine,high,88.49,reviewed +TGAACAAAAAAAAAAA-1|TGAACAAAAAAAAAAA-1,autocrine,high,90.07,reviewed +TGAACAAAAAAAAAAA-1|TTGTAAAAAAAAAAAA-1,paracrine,high,77.22,unreviewed +TGACCAAAAAAAAAAA-1|GGACCAAAAAAAAAAA-1,paracrine,high,99.33,reviewed +TGACCAAAAAAAAAAA-1|TCGCCAAAAAAAAAAA-1,paracrine,mid,56.82,unreviewed +TGACCAAAAAAAAAAA-1|TGACCAAAAAAAAAAA-1,autocrine,mid,53.41,reviewed +TGACCAAAAAAAAAAA-1|TTGACAAAAAAAAAAA-1,paracrine,high,76.61,unreviewed +TGATAAAAAAAAAAAA-1|AATGAAAAAAAAAAAA-1,paracrine,mid,54.84,unreviewed +TGATAAAAAAAAAAAA-1|AGGTAAAAAAAAAAAA-1,paracrine,mid,63.25,reviewed +TGATAAAAAAAAAAAA-1|ATATAAAAAAAAAAAA-1,paracrine,high,80.02,unreviewed +TGATAAAAAAAAAAAA-1|GGATAAAAAAAAAAAA-1,paracrine,mid,62.09,reviewed +TGATAAAAAAAAAAAA-1|TCGTAAAAAAAAAAAA-1,paracrine,high,79.11,unreviewed +TGATAAAAAAAAAAAA-1|TGATAAAAAAAAAAAA-1,autocrine,high,67.75,unreviewed +TGATAAAAAAAAAAAA-1|TTGGAAAAAAAAAAAA-1,paracrine,mid,44.34,unreviewed +TGATCAAAAAAAAAAA-1|ATATCAAAAAAAAAAA-1,paracrine,low,23.54,unreviewed +TGATCAAAAAAAAAAA-1|GCGTCAAAAAAAAAAA-1,paracrine,low,1.34,reviewed +TGATCAAAAAAAAAAA-1|GGATCAAAAAAAAAAA-1,paracrine,low,2.13,reviewed +TGATCAAAAAAAAAAA-1|GTGGCAAAAAAAAAAA-1,paracrine,low,0.37,unreviewed +TGATCAAAAAAAAAAA-1|TCGTCAAAAAAAAAAA-1,paracrine,mid,34.65,reviewed +TGATCAAAAAAAAAAA-1|TGATCAAAAAAAAAAA-1,autocrine,mid,52.44,unreviewed +TGATCAAAAAAAAAAA-1|TTGGCAAAAAAAAAAA-1,paracrine,mid,55.66,reviewed +TGCCCAAAAAAAAAAA-1|ATCCCAAAAAAAAAAA-1,paracrine,high,97.75,unreviewed +TGCCCAAAAAAAAAAA-1|GCTCCAAAAAAAAAAA-1,paracrine,low,2.28,reviewed +TGCCCAAAAAAAAAAA-1|GGCCCAAAAAAAAAAA-1,paracrine,low,1.04,unreviewed +TGCCCAAAAAAAAAAA-1|GTTACAAAAAAAAAAA-1,paracrine,low,1.64,reviewed +TGCCCAAAAAAAAAAA-1|TCTCCAAAAAAAAAAA-1,paracrine,high,70.16,reviewed +TGCCCAAAAAAAAAAA-1|TGCCCAAAAAAAAAAA-1,autocrine,high,85.02,unreviewed +TGCCCAAAAAAAAAAA-1|TTTACAAAAAAAAAAA-1,paracrine,high,78.87,unreviewed +TGCGAAAAAAAAAAAA-1|AAAGAAAAAAAAAAAA-1,paracrine,high,99.7,unreviewed +TGCGAAAAAAAAAAAA-1|AGTGAAAAAAAAAAAA-1,paracrine,high,98.11,reviewed +TGCGAAAAAAAAAAAA-1|ATCGAAAAAAAAAAAA-1,paracrine,low,3.59,unreviewed +TGCGAAAAAAAAAAAA-1|GGCGAAAAAAAAAAAA-1,paracrine,high,66.93,unreviewed +TGCGAAAAAAAAAAAA-1|TCTGAAAAAAAAAAAA-1,paracrine,high,95.68,unreviewed +TGCGAAAAAAAAAAAA-1|TGCGAAAAAAAAAAAA-1,autocrine,high,99.03,unreviewed +TGCGAAAAAAAAAAAA-1|TTTCAAAAAAAAAAAA-1,paracrine,high,88.19,unreviewed +TGCGCAAAAAAAAAAA-1|ATCGCAAAAAAAAAAA-1,paracrine,high,73.39,unreviewed +TGCGCAAAAAAAAAAA-1|GCTGCAAAAAAAAAAA-1,paracrine,high,74.67,unreviewed +TGCGCAAAAAAAAAAA-1|GGCGCAAAAAAAAAAA-1,paracrine,low,31.94,unreviewed +TGCGCAAAAAAAAAAA-1|GTTCCAAAAAAAAAAA-1,paracrine,low,30.02,unreviewed +TGCGCAAAAAAAAAAA-1|TCTGCAAAAAAAAAAA-1,paracrine,high,72.96,reviewed +TGCGCAAAAAAAAAAA-1|TGCGCAAAAAAAAAAA-1,autocrine,mid,55.09,reviewed +TGCGCAAAAAAAAAAA-1|TTTCCAAAAAAAAAAA-1,paracrine,low,26.83,unreviewed +TGGACAAAAAAAAAAA-1|ATGACAAAAAAAAAAA-1,paracrine,mid,54.75,reviewed +TGGACAAAAAAAAAAA-1|GCACCAAAAAAAAAAA-1,paracrine,mid,57.58,reviewed +TGGACAAAAAAAAAAA-1|GGGACAAAAAAAAAAA-1,paracrine,high,67.66,reviewed +TGGACAAAAAAAAAAA-1|GTAACAAAAAAAAAAA-1,paracrine,low,28.47,unreviewed +TGGACAAAAAAAAAAA-1|TCACCAAAAAAAAAAA-1,paracrine,mid,45.83,reviewed +TGGACAAAAAAAAAAA-1|TGGACAAAAAAAAAAA-1,autocrine,low,16.53,unreviewed +TGGACAAAAAAAAAAA-1|TTAACAAAAAAAAAAA-1,paracrine,low,32.8,unreviewed +TGGGCAAAAAAAAAAA-1|AACGCAAAAAAAAAAA-1,paracrine,low,7.06,unreviewed +TGGGCAAAAAAAAAAA-1|AGATCAAAAAAAAAAA-1,paracrine,low,7.8,unreviewed +TGGGCAAAAAAAAAAA-1|ATGGCAAAAAAAAAAA-1,paracrine,low,11.18,reviewed +TGGGCAAAAAAAAAAA-1|GGGGCAAAAAAAAAAA-1,paracrine,low,8.22,unreviewed +TGGGCAAAAAAAAAAA-1|TCATCAAAAAAAAAAA-1,paracrine,low,8.59,unreviewed +TGGGCAAAAAAAAAAA-1|TGGGCAAAAAAAAAAA-1,autocrine,low,13.67,reviewed +TGGGCAAAAAAAAAAA-1|TTAGCAAAAAAAAAAA-1,paracrine,low,7.19,unreviewed +TGGTAAAAAAAAAAAA-1|ATGTAAAAAAAAAAAA-1,paracrine,high,73.75,reviewed +TGGTAAAAAAAAAAAA-1|GCAACAAAAAAAAAAA-1,paracrine,low,0.85,reviewed +TGGTAAAAAAAAAAAA-1|GGGTAAAAAAAAAAAA-1,paracrine,low,1.1,reviewed +TGGTAAAAAAAAAAAA-1|GTATAAAAAAAAAAAA-1,paracrine,low,0.49,unreviewed +TGGTAAAAAAAAAAAA-1|TCAACAAAAAAAAAAA-1,paracrine,high,77.89,unreviewed +TGGTAAAAAAAAAAAA-1|TGGTAAAAAAAAAAAA-1,autocrine,low,23.14,reviewed +TGGTAAAAAAAAAAAA-1|TTATAAAAAAAAAAAA-1,paracrine,low,20.49,reviewed +TGGTCAAAAAAAAAAA-1|AACTCAAAAAAAAAAA-1,paracrine,low,29.78,reviewed +TGGTCAAAAAAAAAAA-1|AGAAGAAAAAAAAAAA-1,paracrine,low,27.8,reviewed +TGGTCAAAAAAAAAAA-1|ATGTCAAAAAAAAAAA-1,paracrine,low,2.38,unreviewed +TGGTCAAAAAAAAAAA-1|GGGTCAAAAAAAAAAA-1,paracrine,mid,59.38,unreviewed +TGGTCAAAAAAAAAAA-1|TCAAGAAAAAAAAAAA-1,paracrine,low,26.37,unreviewed +TGGTCAAAAAAAAAAA-1|TGGTCAAAAAAAAAAA-1,autocrine,low,8.68,reviewed +TGGTCAAAAAAAAAAA-1|TTATCAAAAAAAAAAA-1,paracrine,mid,42.54,reviewed +TGTACAAAAAAAAAAA-1|AAGACAAAAAAAAAAA-1,paracrine,low,21.13,unreviewed +TGTACAAAAAAAAAAA-1|AGCCCAAAAAAAAAAA-1,paracrine,mid,43.45,unreviewed +TGTACAAAAAAAAAAA-1|ATTACAAAAAAAAAAA-1,paracrine,low,22.17,unreviewed +TGTACAAAAAAAAAAA-1|GGTACAAAAAAAAAAA-1,paracrine,mid,41.23,unreviewed +TGTACAAAAAAAAAAA-1|TCCCCAAAAAAAAAAA-1,paracrine,low,11.88,unreviewed +TGTACAAAAAAAAAAA-1|TGTACAAAAAAAAAAA-1,autocrine,low,14.01,unreviewed +TGTACAAAAAAAAAAA-1|TTCACAAAAAAAAAAA-1,paracrine,mid,63.98,unreviewed +TGTCAAAAAAAAAAAA-1|ATTCAAAAAAAAAAAA-1,paracrine,high,72.72,unreviewed +TGTCAAAAAAAAAAAA-1|GCCGAAAAAAAAAAAA-1,paracrine,low,2.98,unreviewed +TGTCAAAAAAAAAAAA-1|GGTCAAAAAAAAAAAA-1,paracrine,low,1.77,reviewed +TGTCAAAAAAAAAAAA-1|TCCGAAAAAAAAAAAA-1,paracrine,mid,60.2,unreviewed +TGTCAAAAAAAAAAAA-1|TGTCAAAAAAAAAAAA-1,autocrine,high,90.16,reviewed +TGTCAAAAAAAAAAAA-1|TTCCAAAAAAAAAAAA-1,paracrine,mid,48.63,unreviewed +TGTCCAAAAAAAAAAA-1|AAGCCAAAAAAAAAAA-1,paracrine,mid,51.52,unreviewed +TGTCCAAAAAAAAAAA-1|AGCGCAAAAAAAAAAA-1,paracrine,low,15.68,unreviewed +TGTCCAAAAAAAAAAA-1|ATTCCAAAAAAAAAAA-1,paracrine,low,2.86,unreviewed +TGTCCAAAAAAAAAAA-1|GGTCCAAAAAAAAAAA-1,paracrine,high,67.39,unreviewed +TGTCCAAAAAAAAAAA-1|TCCGCAAAAAAAAAAA-1,paracrine,mid,64.34,reviewed +TGTCCAAAAAAAAAAA-1|TGTCCAAAAAAAAAAA-1,autocrine,mid,46.04,unreviewed +TGTCCAAAAAAAAAAA-1|TTCCCAAAAAAAAAAA-1,paracrine,high,86.18,reviewed +TGTGAAAAAAAAAAAA-1|ATTGAAAAAAAAAAAA-1,paracrine,high,76.37,unreviewed +TGTGAAAAAAAAAAAA-1|GCCTAAAAAAAAAAAA-1,paracrine,mid,65.35,unreviewed +TGTGAAAAAAAAAAAA-1|GGTGAAAAAAAAAAAA-1,paracrine,mid,55.85,reviewed +TGTGAAAAAAAAAAAA-1|GTCGAAAAAAAAAAAA-1,paracrine,mid,57.25,reviewed +TGTGAAAAAAAAAAAA-1|TCCTAAAAAAAAAAAA-1,paracrine,mid,41.81,reviewed +TGTGAAAAAAAAAAAA-1|TGTGAAAAAAAAAAAA-1,autocrine,mid,34.1,unreviewed +TGTGAAAAAAAAAAAA-1|TTCGAAAAAAAAAAAA-1,paracrine,high,86.36,reviewed +TTAACAAAAAAAAAAA-1|AACACAAAAAAAAAAA-1,paracrine,high,84.65,reviewed +TTAACAAAAAAAAAAA-1|ACTTAAAAAAAAAAAA-1,paracrine,mid,51.61,unreviewed +TTAACAAAAAAAAAAA-1|ATGACAAAAAAAAAAA-1,paracrine,high,81.36,reviewed +TTAACAAAAAAAAAAA-1|GTAACAAAAAAAAAAA-1,paracrine,mid,61.94,unreviewed +TTAACAAAAAAAAAAA-1|TATTAAAAAAAAAAAA-1,paracrine,high,96.32,reviewed +TTAACAAAAAAAAAAA-1|TGGACAAAAAAAAAAA-1,paracrine,mid,33.8,reviewed +TTAACAAAAAAAAAAA-1|TTAACAAAAAAAAAAA-1,autocrine,mid,46.83,unreviewed +TTAGCAAAAAAAAAAA-1|AACGCAAAAAAAAAAA-1,paracrine,high,93.48,unreviewed +TTAGCAAAAAAAAAAA-1|GATCCAAAAAAAAAAA-1,paracrine,high,99.39,reviewed +TTAGCAAAAAAAAAAA-1|GGGGCAAAAAAAAAAA-1,paracrine,high,94.64,unreviewed +TTAGCAAAAAAAAAAA-1|GTAGCAAAAAAAAAAA-1,paracrine,high,99.88,unreviewed +TTAGCAAAAAAAAAAA-1|TATCCAAAAAAAAAAA-1,paracrine,high,99.82,unreviewed +TTAGCAAAAAAAAAAA-1|TGGGCAAAAAAAAAAA-1,paracrine,high,98.23,unreviewed +TTAGCAAAAAAAAAAA-1|TTAGCAAAAAAAAAAA-1,autocrine,high,91.93,reviewed +TTATAAAAAAAAAAAA-1|AACTAAAAAAAAAAAA-1,paracrine,mid,55.09,unreviewed +TTATAAAAAAAAAAAA-1|ACTGAAAAAAAAAAAA-1,paracrine,high,71.56,unreviewed +TTATAAAAAAAAAAAA-1|ATGTAAAAAAAAAAAA-1,paracrine,high,90.5,unreviewed +TTATAAAAAAAAAAAA-1|GTATAAAAAAAAAAAA-1,paracrine,low,1.4,unreviewed +TTATAAAAAAAAAAAA-1|TATGAAAAAAAAAAAA-1,paracrine,high,79.69,unreviewed +TTATAAAAAAAAAAAA-1|TGGTAAAAAAAAAAAA-1,paracrine,mid,47.35,reviewed +TTATAAAAAAAAAAAA-1|TTATAAAAAAAAAAAA-1,autocrine,mid,44.43,unreviewed +TTATCAAAAAAAAAAA-1|AACTCAAAAAAAAAAA-1,paracrine,low,22.53,reviewed +TTATCAAAAAAAAAAA-1|GATGCAAAAAAAAAAA-1,paracrine,high,77.22,unreviewed +TTATCAAAAAAAAAAA-1|GGGTCAAAAAAAAAAA-1,paracrine,mid,43.88,unreviewed +TTATCAAAAAAAAAAA-1|GTATCAAAAAAAAAAA-1,paracrine,mid,55.66,unreviewed +TTATCAAAAAAAAAAA-1|TATGCAAAAAAAAAAA-1,paracrine,high,81.85,unreviewed +TTATCAAAAAAAAAAA-1|TGGTCAAAAAAAAAAA-1,paracrine,low,7.61,unreviewed +TTATCAAAAAAAAAAA-1|TTATCAAAAAAAAAAA-1,autocrine,low,30.21,reviewed +TTCACAAAAAAAAAAA-1|AAGACAAAAAAAAAAA-1,paracrine,mid,64.16,unreviewed +TTCACAAAAAAAAAAA-1|GAAACAAAAAAAAAAA-1,paracrine,mid,54.93,reviewed +TTCACAAAAAAAAAAA-1|GGTACAAAAAAAAAAA-1,paracrine,high,87.82,unreviewed +TTCACAAAAAAAAAAA-1|GTCACAAAAAAAAAAA-1,paracrine,mid,48.42,unreviewed +TTCACAAAAAAAAAAA-1|TAAACAAAAAAAAAAA-1,paracrine,high,73.87,unreviewed +TTCACAAAAAAAAAAA-1|TGTACAAAAAAAAAAA-1,paracrine,mid,50.37,unreviewed +TTCACAAAAAAAAAAA-1|TTCACAAAAAAAAAAA-1,autocrine,high,94.7,reviewed +TTCCAAAAAAAAAAAA-1|AAGCAAAAAAAAAAAA-1,paracrine,low,21.74,unreviewed +TTCCAAAAAAAAAAAA-1|ATTCAAAAAAAAAAAA-1,paracrine,low,24.12,unreviewed +TTCCAAAAAAAAAAAA-1|TGTCAAAAAAAAAAAA-1,paracrine,mid,40.44,unreviewed +TTCCAAAAAAAAAAAA-1|TTCCAAAAAAAAAAAA-1,autocrine,low,9.56,reviewed +TTCCCAAAAAAAAAAA-1|AAGCCAAAAAAAAAAA-1,paracrine,mid,65.56,reviewed +TTCCCAAAAAAAAAAA-1|GAACCAAAAAAAAAAA-1,paracrine,mid,47.87,reviewed +TTCCCAAAAAAAAAAA-1|GGTCCAAAAAAAAAAA-1,paracrine,high,85.38,reviewed +TTCCCAAAAAAAAAAA-1|GTCCCAAAAAAAAAAA-1,paracrine,high,77.95,unreviewed +TTCCCAAAAAAAAAAA-1|TAACCAAAAAAAAAAA-1,paracrine,mid,51.04,unreviewed +TTCCCAAAAAAAAAAA-1|TGTCCAAAAAAAAAAA-1,paracrine,mid,50.76,reviewed +TTCCCAAAAAAAAAAA-1|TTCCCAAAAAAAAAAA-1,autocrine,high,95.89,unreviewed +TTCGAAAAAAAAAAAA-1|AAGGAAAAAAAAAAAA-1,paracrine,low,30.48,unreviewed +TTCGAAAAAAAAAAAA-1|ATTGAAAAAAAAAAAA-1,paracrine,high,78.38,unreviewed +TTCGAAAAAAAAAAAA-1|GTCGAAAAAAAAAAAA-1,paracrine,mid,58.86,reviewed +TTCGAAAAAAAAAAAA-1|TAAGAAAAAAAAAAAA-1,paracrine,low,17.05,reviewed +TTCGAAAAAAAAAAAA-1|TGTGAAAAAAAAAAAA-1,paracrine,mid,36.81,unreviewed 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+TTGGCAAAAAAAAAAA-1|ACCGCAAAAAAAAAAA-1,paracrine,mid,55.66,reviewed +TTGGCAAAAAAAAAAA-1|ATATCAAAAAAAAAAA-1,paracrine,mid,40.13,unreviewed +TTGGCAAAAAAAAAAA-1|GTGGCAAAAAAAAAAA-1,paracrine,low,0.24,reviewed +TTGGCAAAAAAAAAAA-1|TACGCAAAAAAAAAAA-1,paracrine,high,69.15,reviewed +TTGGCAAAAAAAAAAA-1|TGATCAAAAAAAAAAA-1,paracrine,high,77.44,reviewed +TTGGCAAAAAAAAAAA-1|TTGGCAAAAAAAAAAA-1,autocrine,high,79.02,unreviewed +TTGTAAAAAAAAAAAA-1|AATTAAAAAAAAAAAA-1,paracrine,high,76.16,unreviewed +TTGTAAAAAAAAAAAA-1|GACTAAAAAAAAAAAA-1,paracrine,high,92.36,reviewed +TTGTAAAAAAAAAAAA-1|GGAACAAAAAAAAAAA-1,paracrine,mid,61.45,reviewed +TTGTAAAAAAAAAAAA-1|GTGTAAAAAAAAAAAA-1,paracrine,mid,58.01,reviewed +TTGTAAAAAAAAAAAA-1|TACTAAAAAAAAAAAA-1,paracrine,mid,58.13,reviewed +TTGTAAAAAAAAAAAA-1|TGAACAAAAAAAAAAA-1,paracrine,high,86.27,unreviewed +TTGTAAAAAAAAAAAA-1|TTGTAAAAAAAAAAAA-1,autocrine,high,72.66,unreviewed +TTGTCAAAAAAAAAAA-1|ACCTCAAAAAAAAAAA-1,paracrine,high,85.69,unreviewed +TTGTCAAAAAAAAAAA-1|GTGTCAAAAAAAAAAA-1,paracrine,high,70.68,unreviewed +TTGTCAAAAAAAAAAA-1|TACTCAAAAAAAAAAA-1,paracrine,high,75.21,reviewed +TTGTCAAAAAAAAAAA-1|TTGTCAAAAAAAAAAA-1,autocrine,mid,63.79,unreviewed +TTTACAAAAAAAAAAA-1|AAACCAAAAAAAAAAA-1,paracrine,mid,51.28,unreviewed +TTTACAAAAAAAAAAA-1|ACGACAAAAAAAAAAA-1,paracrine,low,31.18,unreviewed +TTTACAAAAAAAAAAA-1|ATCCCAAAAAAAAAAA-1,paracrine,high,76.55,reviewed +TTTACAAAAAAAAAAA-1|GTTACAAAAAAAAAAA-1,paracrine,low,0.18,unreviewed +TTTACAAAAAAAAAAA-1|TAGACAAAAAAAAAAA-1,paracrine,mid,35.75,reviewed +TTTACAAAAAAAAAAA-1|TGCCCAAAAAAAAAAA-1,paracrine,mid,45.49,reviewed +TTTACAAAAAAAAAAA-1|TTTACAAAAAAAAAAA-1,autocrine,mid,36.66,unreviewed +TTTCAAAAAAAAAAAA-1|AAAGAAAAAAAAAAAA-1,paracrine,high,95.49,unreviewed +TTTCAAAAAAAAAAAA-1|GAGCAAAAAAAAAAAA-1,paracrine,mid,58.95,unreviewed +TTTCAAAAAAAAAAAA-1|GGCGAAAAAAAAAAAA-1,paracrine,mid,33.56,reviewed +TTTCAAAAAAAAAAAA-1|GTTCAAAAAAAAAAAA-1,paracrine,mid,53.29,reviewed +TTTCAAAAAAAAAAAA-1|TAGCAAAAAAAAAAAA-1,paracrine,mid,46.65,unreviewed +TTTCAAAAAAAAAAAA-1|TGCGAAAAAAAAAAAA-1,paracrine,high,92.54,reviewed +TTTCAAAAAAAAAAAA-1|TTTCAAAAAAAAAAAA-1,autocrine,mid,64.34,reviewed +TTTCCAAAAAAAAAAA-1|AAAGCAAAAAAAAAAA-1,paracrine,high,68.15,unreviewed +TTTCCAAAAAAAAAAA-1|ACGCCAAAAAAAAAAA-1,paracrine,mid,43.64,unreviewed +TTTCCAAAAAAAAAAA-1|ATCGCAAAAAAAAAAA-1,paracrine,high,87.36,unreviewed +TTTCCAAAAAAAAAAA-1|GTTCCAAAAAAAAAAA-1,paracrine,mid,62.18,reviewed +TTTCCAAAAAAAAAAA-1|TAGCCAAAAAAAAAAA-1,paracrine,high,83.5,unreviewed +TTTCCAAAAAAAAAAA-1|TGCGCAAAAAAAAAAA-1,paracrine,high,77.74,reviewed +TTTCCAAAAAAAAAAA-1|TTTCCAAAAAAAAAAA-1,autocrine,mid,49.48,unreviewed From da175647e6dd4ecf20b05264cbac075ef1b73ca7 Mon Sep 17 00:00:00 2001 From: Micha Sam Brickman Raredon <42100128+msraredon@users.noreply.github.com> Date: Fri, 7 Aug 2026 10:22:04 -0400 Subject: [PATCH 04/13] fix: revalidate index.html so a rebuild actually reaches the browser MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit nginx served index.html with only Last-Modified/ETag and no Cache-Control, so browsers fell back to heuristic caching and could reuse it for hours without revalidating. index.html is the only file that names the current bundle. Serving a stale one after `docker compose up --build` leaves the tab running the previous JS: the app looks healthy, reports the new version from the backend — that comes over the API, not the bundle — and silently lacks every frontend fix in the update. That is what "I rebuilt and my change isn't there" looks like from the outside, and it is what made the autocrine colour fix appear absent on :3000 when the container was in fact serving the corrected bundle all along. index.html -> Cache-Control: no-cache Revalidate before use. Not "do not store" — the usual response is a 304, so the cost is one round trip on the smallest file we serve. /assets/ -> Cache-Control: public, max-age=31536000, immutable Vite fingerprints these by content, so the name changes whenever the bytes do. Caching them forever is what makes revalidating the HTML cheap. Co-Authored-By: Claude Opus 5 --- frontend/nginx.conf | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/frontend/nginx.conf b/frontend/nginx.conf index ac8202c..5b52f3f 100644 --- a/frontend/nginx.conf +++ b/frontend/nginx.conf @@ -23,8 +23,30 @@ server { # Hashed static assets — serve directly; never fall back to index.html. # Vite fingerprints every file in /assets/ so a 404 here means the Docker # image is stale (use --no-cache to force a clean rebuild). + # + # The filename changes whenever the contents do, so these can be cached hard + # and forever. That is the half of the contract that makes the no-cache on + # index.html below cheap: only the tiny HTML is ever re-fetched. location /assets/ { try_files $uri =404; + add_header Cache-Control "public, max-age=31536000, immutable"; + } + + # index.html must be revalidated on every load, and this is load-bearing. + # + # It is the only file that names the current bundle. Without an explicit + # Cache-Control, nginx sends just Last-Modified/ETag and browsers fall back + # to *heuristic* caching — they may serve a stale index.html for hours + # without asking. After a rebuild that means the tab keeps running the + # previous bundle: the app looks fine, reports the new version string from + # the backend, and silently lacks every frontend fix in the update. That is + # exactly what "I rebuilt and my change isn't there" looks like, and it cost + # a debugging session to track down. + # + # `no-cache` does not mean "do not store" — it means "revalidate before + # using", so the usual response is a 304 and the cost is one round trip. + location = /index.html { + add_header Cache-Control "no-cache"; } # SPA fallback for all other routes From 0b31c985859e5cbed9973fc00dc5cea3d9231285 Mon Sep 17 00:00:00 2001 From: Micha Sam Brickman Raredon <42100128+msraredon@users.noreply.github.com> Date: Fri, 7 Aug 2026 11:24:30 -0400 Subject: [PATCH 05/13] feat: split screen can compare two different datasets MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Split screen showed two views of one dataset — a single global `dataset`, a single dataset picker, a single `imageSize`. Each panel can now show a different dataset, including a different platform. Per-panel state Everything bound to *which dataset a panel shows* moves into panels[panelIndex]: dataset, activeImage, imageSize, platformCapabilities, pixelSize, edgeFile, lrmCatalogue, allGenes, the colour ranges, and the shown/total stats. Each differs between datasets, so none of them could stay global. Style and choice settings stay shared on purpose. One sidebar driving both panels is what makes a comparison comparable, and it avoids the focus model — "which panel is the sidebar editing?" — that is the genuinely hard part of full independence. Phase 2 splits these behind sidebar tabs. Reconciling one sidebar across two datasets — hooks/usePanels.js The rule is union, then degrade per panel: offer a control when either panel can use it, and let the panel that cannot render nothing. Intersecting would hide controls that work fine on one side, which is worse when the whole point is comparing unlike things. Gene panels, metadata columns, edge columns and LRM catalogues are all unioned; unit_label becomes the neutral "unit" when panels disagree, because calling a Visium spot a "cell" would be worse than saying nothing. Match zoom now matches PHYSICAL scale It divided both viewports by the local image width — "the same proportion of the picture". Identical behaviour when both panels showed one dataset, and meaningless across two: 20% of a 6.5 mm Visium capture area and 20% of a 55 µm seqFISH ROI differ by 55x. It now converts through each panel's own pixel_size, so the same number of microns spans the same screen width, as a scalebar would. Verified Visium <-> seqFISH: 2996.9 µm and 54.9 µm across, matched to 54.9 µm both — at which point one 55 µm Visium spot fills the same field as the entire seqFISH ROI. Shared colour scale, on by default Two viridis panels that each auto-ranged to their own data look comparable and are not: one's yellow might be 40 counts and the other's 4,000. Linked, both clamp to the union of the two ranges so the single legend is true for everything on screen. Verified [131,547] and [66,447] unioning to [66,547]. Unlockable, with the panel saying plainly that colours are then not comparable. An explicit slider clamp always wins. Also - Dataset / image / edge-source pickers move into each panel's header in split mode; an image name or edge file only means something relative to one dataset. Single-panel mode is unchanged, pickers still in the sidebar. - Selection carries its panel index, so CellInfoPanel, EdgeInfoPanel and region export resolve against the dataset that was actually clicked. EdgeInfoPanel used to be pinned to panel 0. - DatasetPicker is its own module, breaking the cycle it would otherwise close: Viewer -> LayerPanel -> App -> Viewer. Known trade: changing either panel's dataset resets the shared column-named settings (filters, colour-by field, gene allowlist, hidden LRMs), because a filter naming a column the new dataset lacks 400s on every viewport change. That clears the other panel's filter too, and goes away in Phase 2. v0.9.0. Guard unaffected (238 probes, reader-level). Co-Authored-By: Claude Opus 5 --- CLAUDE.md | 40 ++ backend/app/main.py | 2 +- frontend/package.json | 2 +- frontend/src/components/CellInfoPanel.jsx | 8 +- frontend/src/components/DatasetPicker.jsx | 163 ++++++++ frontend/src/components/LayerPanel.jsx | 462 +++++++++++----------- frontend/src/components/Viewer.jsx | 223 ++++++++--- frontend/src/hooks/usePanels.js | Bin 0 -> 3418 bytes frontend/src/store.js | 223 ++++++----- 9 files changed, 747 insertions(+), 376 deletions(-) create mode 100644 frontend/src/components/DatasetPicker.jsx create mode 100644 frontend/src/hooks/usePanels.js diff --git a/CLAUDE.md b/CLAUDE.md index aadf61a..61c3dab 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -526,6 +526,46 @@ Results set `selectedCell` or `selectedEdge` in the store. - **`Viewer`** (default export) — thin wrapper; renders `` always, plus `` when `panelCount >= 2`. +**Two panels can now show two different datasets.** Everything bound to *which +dataset a panel shows* lives in `panels[panelIndex]` (store.js `makePanel()`): +`dataset`, `activeImage`, `imageSize`, `platformCapabilities`, `pixelSize`, +`edgeFile`, `lrmCatalogue`, `allGenes`, the colour ranges, and the shown/total +stats. Each of those differs between datasets, so none of them can be global. + +Style and choice settings — layer opacity, palettes, colour-by, filters, LRM +selection, edge geometry — deliberately stay **shared**: one sidebar drives both +panels, which is what makes a side-by-side comparison comparable. The sidebar +reconciles across panels with `hooks/usePanels.js`, whose rule is **union, then +degrade per panel**: offer a control if *either* panel can use it, and let the +panel that cannot render nothing. Intersecting instead would hide controls that +work perfectly well on one side, which is worse when the point is comparing +unlike things. `unit_label` becomes the neutral "unit" when the panels disagree. + +Consequences worth knowing: + +- **The dataset / image / edge-source pickers move into each panel's header** in + split mode, because an image name or edge file only means something relative to + one dataset. In single-panel mode they stay in the sidebar, unchanged. +- **Changing either panel's dataset resets the shared column-, gene- and + mechanism-named settings** (filters, colour-by field, gene allowlist, hidden + LRMs). It has to: a filter naming a column the new dataset lacks 400s on every + viewport change. The cost is that switching one panel clears the other's + filter. That goes away when these become per-panel. +- **Selection carries its panel index** (`selection = {panelIndex, kind, …}`), so + `CellInfoPanel` / `EdgeInfoPanel` and region export resolve against the dataset + that was actually clicked. `EdgeInfoPanel` used to be pinned to panel 0. +- **⇔ Match zoom matches physical scale, not fraction of image.** It used to + divide both viewports by the local image width, i.e. match "the same proportion + of the picture" — identical behaviour when both panels showed one dataset, and + meaningless across two. 20% of a 6.5 mm Visium capture area and 20% of a 55 µm + seqFISH ROI differ by 55×. It now converts through each panel's own `pixelSize` + so the same number of microns spans the same screen width, exactly as a + scalebar would. Verified across Visium↔seqFISH: 2997 µm vs 55 µm → 55 µm both. +- **`linkColorScale` (default on) shares one colour range across panels.** This + is figure integrity, not preference: two viridis panels that each auto-ranged + to their own data look comparable and are not — one's yellow might be 40 counts + and the other's 4,000. An explicit clamp from the sliders always wins. + **What is per-panel (local state / per-instance):** - OSD viewer instance (`viewerRef`) - deck.gl ref (`deckRef`) diff --git a/backend/app/main.py b/backend/app/main.py index 124a35a..701e166 100644 --- a/backend/app/main.py +++ b/backend/app/main.py @@ -3,7 +3,7 @@ from app.routers import tiles, spatial, edges, layers -APP_VERSION = "0.8.3" +APP_VERSION = "0.9.0" app = FastAPI(title="TissuePlex API", version=APP_VERSION) diff --git a/frontend/package.json b/frontend/package.json index 76f4836..28e6b20 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -1,6 +1,6 @@ { "name": "tissueplex", - "version": "0.8.3", + "version": "0.9.0", "private": true, "scripts": { "dev": "vite", diff --git a/frontend/src/components/CellInfoPanel.jsx b/frontend/src/components/CellInfoPanel.jsx index 18a4b68..c009f25 100644 --- a/frontend/src/components/CellInfoPanel.jsx +++ b/frontend/src/components/CellInfoPanel.jsx @@ -10,7 +10,13 @@ const KEY = { color: "#666" }; const VAL = { color: "#ccc", textAlign: "right", marginLeft: 8, wordBreak: "break-all" }; export default function CellInfoPanel() { - const { apiBase, dataset, selectedCell, colorBy, cellColorEnabled, selectedGenes, platformCapabilities } = useStore(); + const { apiBase, colorBy, cellColorEnabled, selectedGenes, selection } = useStore(); + // Resolve against the panel that produced the click, not panel 0 — with two + // datasets on screen, panel 0's would be the wrong one half the time. + const selectedCell = selection?.kind === "cell" ? selection.cell : null; + const owner = useStore((s) => s.panels[selection?.panelIndex ?? 0]); + const dataset = owner?.dataset; + const platformCapabilities = owner?.platformCapabilities; const [detail, setDetail] = useState(null); const [loading, setLoading] = useState(false); diff --git a/frontend/src/components/DatasetPicker.jsx b/frontend/src/components/DatasetPicker.jsx new file mode 100644 index 0000000..3532f84 --- /dev/null +++ b/frontend/src/components/DatasetPicker.jsx @@ -0,0 +1,163 @@ +/** + * Dataset / image / edge-source picker for ONE panel. + * + * Its own module because both LayerPanel (single-panel mode, in the sidebar) + * and Viewer (split mode, in each panel header) render it. Importing it from + * LayerPanel instead would close the cycle Viewer -> LayerPanel -> App -> Viewer, + * which ESM tolerates but which resolves to `undefined` often enough to be worth + * avoiding outright. + */ +import React, { useEffect, useState } from "react"; +import { useStore } from "../store"; + +const SECTION_HEADER = { + fontSize: 10, fontFamily: "monospace", color: "#555", + textTransform: "uppercase", letterSpacing: 1, + marginTop: 14, marginBottom: 6, paddingBottom: 3, + borderBottom: "1px solid #2a2a2a", +}; + +const SELECT_STYLE = { + background: "#252525", color: "#ccc", border: "1px solid #3a3a3a", + borderRadius: 3, padding: "2px 4px", fontFamily: "monospace", + fontSize: 11, cursor: "pointer", width: "100%", +}; + +/** + * Dataset + image picker for ONE panel. + * + * Exported because in split mode it renders inside each panel's header rather + * than in the sidebar: with two panels able to show two datasets, a single + * sidebar picker has no well-defined meaning. In single-panel mode LayerPanel + * still renders it at the top, where it has always been. + */ +export function DatasetPicker({ panelIndex = 0, compact = false }) { + const apiBase = useStore((s) => s.apiBase); + const setPanelDataset = useStore((s) => s.setPanelDataset); + const patchPanel = useStore((s) => s.patchPanel); + const { dataset, activeImage } = useStore((s) => s.panels[panelIndex]); + const setPanelEdgeFile = useStore((s) => s.setPanelEdgeFile); + const edgeFile = useStore((s) => s.panels[panelIndex].edgeFile); + const [datasets, setDatasets] = useState([]); + const [images, setImages] = useState([]); + const [edgeFiles, setEdgeFiles] = useState([]); + + // Fetch dataset list; auto-initialize to first entry if this panel has none. + useEffect(() => { + fetch(`${apiBase}/spatial/datasets`) + .then((r) => r.ok ? r.json() : []) + .then((list) => { + if (!Array.isArray(list)) return; + setDatasets(list); + if (list.length > 0) { + const cur = useStore.getState().panels[panelIndex].dataset; + if (!cur || !list.includes(cur)) setPanelDataset(panelIndex, list[0]); + } + }) + .catch(() => {}); + }, [apiBase, panelIndex]); // eslint-disable-line react-hooks/exhaustive-deps + + // Fetch available images for this panel's dataset + useEffect(() => { + if (!dataset) return; + fetch(`${apiBase}/spatial/${dataset}/images`) + .then((r) => r.ok ? r.json() : []) + .then((list) => { if (Array.isArray(list)) setImages(list); }) + .catch(() => setImages([])); + }, [apiBase, dataset]); // eslint-disable-line react-hooks/exhaustive-deps + + // Edge sources for this panel's dataset (issue #46). Kept beside the dataset + // picker because an edge file only means anything relative to one dataset. + useEffect(() => { + if (!dataset) { setEdgeFiles([]); return; } + fetch(`${apiBase}/edges/${dataset}/files`) + .then((r) => (r.ok ? r.json() : null)) + .then((data) => { + const files = Array.isArray(data?.files) ? data.files : []; + setEdgeFiles(files); + const ids = files.map((f) => f.id); + const cur = useStore.getState().panels[panelIndex].edgeFile; + if (files.length > 0 && !ids.includes(cur)) { + setPanelEdgeFile(panelIndex, data.default ?? ids[0]); + } + }) + .catch(() => setEdgeFiles([])); + }, [apiBase, dataset, panelIndex]); // eslint-disable-line react-hooks/exhaustive-deps + + // Keep activeImage valid for whatever images the current dataset offers. + // This is a separate, declarative effect rather than a branch inside the fetch + // above because the fetch only re-runs on dataset change: with activeImage + // captured in its closure, whether it got set depended on the order the two + // state updates landed in, which left datasets whose only image is the + // synthesised placeholder with activeImage stuck at null and no viewer at all. + useEffect(() => { + if (images.length > 0 && !images.includes(activeImage)) { + patchPanel(panelIndex, { activeImage: images[0] }); + } + }, [images, activeImage, panelIndex]); // eslint-disable-line react-hooks/exhaustive-deps + + if (datasets.length === 0) { + return ( +
      + Connecting… +
      + ); + } + + const sel = compact + ? { ...SELECT_STYLE, fontSize: 10, padding: "1px 3px", width: "auto", maxWidth: 190 } + : SELECT_STYLE; + + const edgePicker = edgeFiles.length > 1 && ( + + ); + + if (compact) { + // Panel header: one line, no section labels — the panel it sits on is the label. + return ( +
      + + {images.length > 1 && ( + + )} + {edgePicker} +
      + ); + } + + return ( +
      +
      Dataset
      + + {images.length > 1 && ( + <> +
      Image
      + + + )} + {edgeFiles.length > 1 && ( + <> +
      Edge source
      + {edgePicker} + + )} +
      + ); +} diff --git a/frontend/src/components/LayerPanel.jsx b/frontend/src/components/LayerPanel.jsx index f7a469a..8532fe9 100644 --- a/frontend/src/components/LayerPanel.jsx +++ b/frontend/src/components/LayerPanel.jsx @@ -3,6 +3,8 @@ */ import React, { useEffect, useRef, useState } from "react"; import { useStore } from "../store"; +import { useActiveDatasets, useActivePanels, useUnionCapabilities, useUnionList } from "../hooks/usePanels"; +import { DatasetPicker } from "./DatasetPicker"; import { APP_VERSION } from "../App"; import { legendGradient, QUAL_PALETTE } from "../utils/colormap"; import { geneColor } from "../utils/geneColor"; @@ -58,96 +60,17 @@ const INPUT_STYLE = { const PALETTE_OPTIONS = ["viridis", "plasma", "magma", "inferno"]; -function DatasetPicker() { - const { apiBase, dataset, setDataset, activeImage, setActiveImage } = useStore(); - const [datasets, setDatasets] = useState([]); - const [images, setImages] = useState([]); - - // Fetch dataset list; auto-initialize to first entry if store has no valid dataset - useEffect(() => { - fetch(`${apiBase}/spatial/datasets`) - .then((r) => r.ok ? r.json() : []) - .then((list) => { - if (!Array.isArray(list)) return; - setDatasets(list); - if (list.length > 0) { - const cur = useStore.getState().dataset; - if (!cur || !list.includes(cur)) setDataset(list[0]); - } - }) - .catch(() => {}); - }, [apiBase]); // eslint-disable-line react-hooks/exhaustive-deps - - // Fetch available images for the current dataset - useEffect(() => { - if (!dataset) return; - fetch(`${apiBase}/spatial/${dataset}/images`) - .then((r) => r.ok ? r.json() : []) - .then((list) => { - if (!Array.isArray(list)) return; - setImages(list); - }) - .catch(() => setImages([])); - }, [apiBase, dataset]); // eslint-disable-line react-hooks/exhaustive-deps - - // Keep activeImage valid for whatever images the current dataset offers. - // This is a separate, declarative effect rather than a branch inside the fetch - // above because the fetch only re-runs on dataset change: with activeImage - // captured in its closure, whether it got set depended on the order the two - // state updates landed in, which left datasets whose only image is the - // synthesised placeholder with activeImage stuck at null and no viewer at all. - useEffect(() => { - if (images.length > 0 && !images.includes(activeImage)) { - setActiveImage(images[0]); - } - }, [images, activeImage]); // eslint-disable-line react-hooks/exhaustive-deps - - if (datasets.length === 0) { - return ( -
      - Connecting… -
      - ); - } - - return ( -
      -
      Dataset
      - - {images.length > 1 && ( - <> -
      Image
      - - - )} -
      - ); -} - export default function LayerPanel() { - const { platformCapabilities, apiBase } = useStore(); - const hasTranscripts = platformCapabilities?.has_transcripts ?? true; - const hasBoundaries = platformCapabilities?.has_boundaries ?? true; + const apiBase = useStore((s) => s.apiBase); + const panelCount = useStore((s) => s.panelCount); + // Union across visible panels: offer a layer when either panel can serve it. + const caps = useUnionCapabilities(); + const hasTranscripts = caps.has_transcripts; + const hasBoundaries = caps.has_boundaries; // A dataset with no morphology gets a placeholder canvas, so the opacity // control would be a dead toggle over a flat fill. - const hasMorphology = platformCapabilities?.has_morphology ?? true; - const unitLabel = platformCapabilities?.unit_label ?? "cell"; + const hasMorphology = caps.has_morphology; + const unitLabel = caps.unit_label; const unitTitle = unitLabel.charAt(0).toUpperCase() + unitLabel.slice(1); // Show the build-time version immediately; check the backend version via /health @@ -175,7 +98,13 @@ export default function LayerPanel() { display: "flex", flexDirection: "column", }}> - + {/* In split mode each panel header carries its own picker, since the two + panels can show different datasets. */} + {panelCount < 2 ? : ( +
      + Comparing {panelCount} panels — pick each dataset in its header +
      + )}
      Layers
      Core
      @@ -186,6 +115,9 @@ export default function LayerPanel() {
      {unitTitle} Color
      + {/* Shared colour scale — only meaningful with two panels. */} + {panelCount >= 2 && } + {/* Issue #45. Placed under the color section because picking the column to subset on is the same act as picking the column to colour by, and users almost always do the two together. */} @@ -222,43 +154,68 @@ export default function LayerPanel() { ); } + +/** + * `{column: dtype}` merged across the visible panels. + * + * Only used to decide whether a column is numeric, which is what gates the + * "treat as categorical" checkbox. Panel 0 wins a dtype disagreement — the + * checkbox only needs to know numeric-or-not, and the backend is the authority + * on the actual typing either way. + */ +function useSchemaDtypes() { + const apiBase = useStore((s) => s.apiBase); + const datasets = useActiveDatasets(); + const key = datasets.join(" "); + const [map, setMap] = React.useState({}); + React.useEffect(() => { + if (!datasets.length) { setMap({}); return; } + let cancelled = false; + Promise.all(datasets.map((d) => + fetch(`${apiBase}/spatial/${d}/cells/schema`) + .then((r) => (r.ok ? r.json() : null)).catch(() => null))) + .then((rs) => { + if (cancelled) return; + const out = {}; + for (const r of [...rs].reverse()) Object.assign(out, r?.columns ?? {}); + setMap(out); + }); + return () => { cancelled = true; }; + }, [key, apiBase]); + return map; +} + // ── Color By section ────────────────────────────────────────────────────────── function ColorBySection({ unitLabel = "cell" }) { const { - platformCapabilities, - apiBase, dataset, + apiBase, cellColorEnabled, setCellColorEnabled, colorBy, setColorBy, cellColorPalette, setCellColorPalette, - allGenes, setAllGenes, setGenesLoaded, selectedGenes, - cellColorRange, cellColorClamp, setCellColorClamp, categoricalOverrides, setCategoricalOverride, - cellColorType, cellColorCategories, } = useStore(); - const [cellSchema, setCellSchema] = useState(null); - const hasTranscripts = platformCapabilities?.has_transcripts ?? true; + // Genes and metadata columns are unioned across the visible panels, so a + // column that exists only in panel 1 is still selectable. A panel that lacks + // the chosen column simply renders nothing for it. + const allGenes = useUnionList((d) => + fetch(`${apiBase}/spatial/${d}/genes`).then((r) => (r.ok ? r.json() : []))); + const schemaColumns = useUnionList((d) => + fetch(`${apiBase}/spatial/${d}/cells/schema`) + .then((r) => (r.ok ? r.json() : null)) + .then((x) => (x?.columns ? Object.keys(x.columns) : []))); + const schemaDtypes = useSchemaDtypes(); + // The legend describes panel 0; with a shared colour scale (see Viewer) both + // panels use the same range, so one legend is accurate for both. + const { cellColorRange, cellColorType, cellColorCategories } = + useStore((s) => s.panels[0]); + const caps = useUnionCapabilities(); + + const hasTranscripts = caps.has_transcripts; const unitTitle = unitLabel.charAt(0).toUpperCase() + unitLabel.slice(1); - // Fetch full gene list and cell schema once per dataset - useEffect(() => { - // `dataset` is null until DatasetPicker resolves the list; fetching then - // just 404s against /spatial/null/... and buries real errors in the console. - if (!dataset) return; - setGenesLoaded(false); - fetch(`${apiBase}/spatial/${dataset}/genes`) - .then((r) => r.ok ? r.json() : []) - .then((g) => { if (Array.isArray(g)) setAllGenes(g); }) - .catch(() => {}) - .finally(() => setGenesLoaded(true)); - fetch(`${apiBase}/spatial/${dataset}/cells/schema`) - .then((r) => r.ok ? r.json() : null) - .then((s) => { if (s) setCellSchema(s); }) - .catch(() => {}); - }, [apiBase, dataset]); // eslint-disable-line react-hooks/exhaustive-deps - const { mode, field } = colorBy; const selectedCount = selectedGenes === null ? allGenes.length : selectedGenes.size; @@ -267,7 +224,7 @@ function ColorBySection({ unitLabel = "cell" }) { // schema dtype: the backend also treats a low-cardinality integer column as // categorical, so guessing from dtype used to draw a gradient legend with two // dead sliders over a canvas that was already showing discrete colours. - const fieldDtype = field && cellSchema ? cellSchema.columns[field] : null; + const fieldDtype = field ? schemaDtypes[field] : null; const isCategorical = mode === "metadata" && !!field && cellColorType === "categorical"; // The override is only meaningful for a numeric column — a text column has no @@ -325,7 +282,7 @@ function ColorBySection({ unitLabel = "cell" }) { style={{ ...SELECT_STYLE, marginTop: 4 }} > - {cellSchema?.columns && Object.keys(cellSchema.columns).map((c) => ( + {schemaColumns.map((c) => ( ))} @@ -589,6 +546,29 @@ function CategoricalLegend({ field, categories = [] }) { ); } + +/** + * Merge /color-values responses from several datasets into one. + * + * A shared filter needs the union of what either panel can show: every category + * from both, and the widest numeric range. Datasets lacking the column + * contribute nothing rather than erroring, which is how a filter on a + * panel-1-only column still works while panel 0 simply draws everything. + */ +async function mergeColorValues(promises) { + const rs = (await Promise.all(promises)).filter(Boolean); + if (!rs.length) return null; + if (rs.some((r) => r.type === "categorical")) { + const seen = new Set(), cats = []; + for (const r of rs) for (const c of r.categories ?? []) + if (!seen.has(c)) { seen.add(c); cats.push(c); } + return { type: "categorical", categories: cats }; + } + const mins = rs.map((r) => r.min).filter((v) => v != null); + const maxs = rs.map((r) => r.max).filter((v) => v != null); + return { type: "continuous", min: Math.min(...mins), max: Math.max(...maxs) }; +} + // ── Metadata filter (issue #45) ─────────────────────────────────────────────── /** * Restrict the view to a subset of units by one metadata column. @@ -728,27 +708,27 @@ function fmtBound(v) { } function CellFilterSection({ unitLabel = "cell" }) { - const { apiBase, dataset, cellFilter, setCellFilter, categoricalOverrides } = useStore(); - const [columns, setColumns] = useState([]); - - useEffect(() => { - if (!dataset) return; - fetch(`${apiBase}/spatial/${dataset}/cells/schema`) + const { apiBase, cellFilter, setCellFilter, categoricalOverrides } = useStore(); + const datasets = useActiveDatasets(); + const columns = useUnionList((d) => + fetch(`${apiBase}/spatial/${d}/cells/schema`) .then((r) => (r.ok ? r.json() : null)) - .then((s) => setColumns(s?.columns ? Object.keys(s.columns) : [])) - .catch(() => setColumns([])); - }, [apiBase, dataset]); + .then((x) => (x?.columns ? Object.keys(x.columns) : []))); // Honour the same categorical override the color panel uses, so a column the // user has declared categorical offers checkboxes here rather than a range. + // Values are pooled across the visible panels: the filter is shared, so its + // categories must cover every value either panel can show. A dataset without + // the column contributes nothing rather than erroring. const fetchValues = React.useCallback((field) => { const categorical = categoricalOverrides[`cell::${field}`] ?? null; - return fetch(`${apiBase}/spatial/${dataset}/color-values`, { - method: "POST", - headers: { "Content-Type": "application/json" }, - body: JSON.stringify({ mode: "metadata", field, categorical }), - }).then((r) => (r.ok ? r.json() : null)); - }, [apiBase, dataset, categoricalOverrides]); + return mergeColorValues(datasets.map((d) => + fetch(`${apiBase}/spatial/${d}/color-values`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ mode: "metadata", field, categorical }), + }).then((r) => (r.ok ? r.json() : null)).catch(() => null))); + }, [apiBase, datasets.join(" "), categoricalOverrides]); // eslint-disable-line return ( p.dataset) + .map((p) => ({ dataset: p.dataset, ef: `?edge_file=${encodeURIComponent(p.edgeFile)}` })); + const srcKey = sources.map((x) => x.dataset + x.ef).join(" "); useEffect(() => { - if (!dataset) return; - fetch(`${apiBase}/edges/${dataset}/schema${efParam}`) - .then((r) => (r.ok ? r.json() : null)) - // Structural and per-LRM columns are not edge attributes to subset on: - // one edge has many LRM rows, so "lrm = X" is a mechanism filter, which - // the LRM checklist below already does properly. - .then((s) => setColumns( - s?.columns - ? Object.keys(s.columns).filter((c) => !EDGE_FILTER_SKIP.has(c)) - : [] - )) - .catch(() => setColumns([])); - }, [apiBase, dataset, efParam]); + if (!sources.length) { setColumns([]); return; } + Promise.all(sources.map(({ dataset, ef }) => + fetch(`${apiBase}/edges/${dataset}/schema${ef}`) + .then((r) => (r.ok ? r.json() : null)).catch(() => null))) + .then((rs) => { + const seen = new Set(), out = []; + for (const r of rs) for (const c of Object.keys(r?.columns ?? {})) + if (!EDGE_FILTER_SKIP.has(c) && !seen.has(c)) { seen.add(c); out.push(c); } + setColumns(out); + }); + }, [apiBase, srcKey]); // eslint-disable-line react-hooks/exhaustive-deps const fetchValues = React.useCallback((field) => { const categorical = categoricalOverrides[`edge::${field}`] ?? null; - return fetch(`${apiBase}/edges/${dataset}/edge-color-values${efParam}`, { - method: "POST", - headers: { "Content-Type": "application/json" }, - body: JSON.stringify({ mode: "metadata", field, categorical }), - }).then((r) => (r.ok ? r.json() : null)); - }, [apiBase, dataset, efParam, categoricalOverrides]); + return mergeColorValues(sources.map(({ dataset, ef }) => + fetch(`${apiBase}/edges/${dataset}/edge-color-values${ef}`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ mode: "metadata", field, categorical }), + }).then((r) => (r.ok ? r.json() : null)).catch(() => null))); + }, [apiBase, srcKey, categoricalOverrides]); // eslint-disable-line if (!columns.length) return null; return ( @@ -801,6 +783,39 @@ const EDGE_FILTER_SKIP = new Set([ "lrm", "lrm_id", "ligand", "receptor", "score", "score_norm", ]); + + +/** + * Toggle for the cross-panel colour scale. + * + * Shown only in split mode. Default on, because independent auto-ranging makes + * two panels look comparable when they are not — see the store comment. + */ +function LinkColorScaleRow() { + const { linkColorScale, setLinkColorScale } = useStore(); + return ( + + ); +} + +/** Sum a per-panel {shown,total} stat over the visible panels. */ +function useSummedStat(key) { + const panelCount = useStore((s) => s.panelCount); + const panels = useStore((s) => s.panels); + return React.useMemo(() => panels.slice(0, panelCount).reduce( + (a, p) => ({ shown: a.shown + (p[key]?.shown ?? 0), total: a.total + (p[key]?.total ?? 0) }), + { shown: 0, total: 0 }), [panels, panelCount, key]); +} + // ── Morphology row ──────────────────────────────────────────────────────────── function MorphologyRow() { const { layers, setLayerProp } = useStore(); @@ -861,7 +876,10 @@ function LayerRowBase({ label, color, visible, opacity, onToggle, onOpacity }) { } function TranscriptLayerRow() { - const { layers, setLayerProp, transcriptFraction, setTranscriptFraction, transcriptStats } = useStore(); + const { layers, setLayerProp, transcriptFraction, setTranscriptFraction } = useStore(); + // Summed across the visible panels: with two datasets, "how much is on + // screen" is the total of both, and one number is less noise than two. + const transcriptStats = useSummedStat("transcriptStats"); const state = layers.transcripts ?? { visible: true, opacity: 0.8 }; const { shown, total } = transcriptStats; @@ -925,8 +943,8 @@ function CellSegmentsRow({ unitTitle = "Cell" }) { const { layers, setLayerProp, cellBoundaryFraction, setCellBoundaryFraction, - cellBoundaryStats, } = useStore(); + const cellBoundaryStats = useSummedStat("cellBoundaryStats"); const state = layers.cellSegments ?? { visible: true, opacity: 0.6, outlineOpacity: 0.8 }; const { shown, total } = cellBoundaryStats; const pctShown = total > 0 ? (shown / total * 100) : null; @@ -1016,10 +1034,17 @@ function CellSegmentsRow({ unitTitle = "Cell" }) { // ── Transcript species section ──────────────────────────────────────────────── function TranscriptSpeciesSection() { const { - allGenes, genesLoaded, selectedGenes, setSelectedGenes, toggleSelectedGene, + selectedGenes, setSelectedGenes, toggleSelectedGene, transcriptColorOverrides, setTranscriptColorOverride, mergeTranscriptColorOverrides, resetTranscriptColorOverrides, } = useStore(); + const apiBase = useStore((s) => s.apiBase); + const datasets = useActiveDatasets(); + // Union across panels: a 960-gene CosMx panel beside a 130-gene MERSCOPE one + // offers both, and each panel renders only the genes it actually measures. + const allGenes = useUnionList((d) => + fetch(`${apiBase}/spatial/${d}/genes`).then((r) => (r.ok ? r.json() : []))); + const genesLoaded = datasets.length === 0 || allGenes.length > 0; const [expanded, setExpanded] = useState(false); const [search, setSearch] = useState(""); const fileInputRef = useRef(null); @@ -1261,8 +1286,7 @@ const EDGE_SKIP_COLS = new Set(["x1", "y1", "x2", "y2", "edge", "sending_cell", function EdgeSection() { const { - apiBase, dataset, - edgeFile, setEdgeFile, + apiBase, layers, setLayerProp, edgeMinStrength, setEdgeMinStrength, edgeColorBy, setEdgeColorBy, @@ -1276,54 +1300,58 @@ function EdgeSection() { showArrowheads, setShowArrowheads, arrowStyle, setArrowStyle, arrowheadScale, setArrowheadScale, - lrmCatalogue, setLrmCatalogue, hiddenLrms, toggleLrm, setAllLrmsVisible, hideAllLrms, - edgeColorRange, edgeColorClamp, setEdgeColorClamp, categoricalOverrides, setCategoricalOverride, } = useStore(); + const active = useActivePanels(); const state = layers.edges ?? { visible: true, opacity: 0.9 }; const [localStrength, setLocalStrength] = useState(edgeMinStrength ?? 0); const commitTimer = useRef(null); - const [edgeSchema, setEdgeSchema] = useState(null); const [lrmSearch, setLrmSearch] = useState(""); - // List of edge-source files in the current dataset: [{id, label}]. - const [edgeFiles, setEdgeFiles] = useState([]); - // ── edge-source parquet list (issue #46) ──────────────────────────────────── - // Fetch the available edge files for the dataset and select the backend-provided - // default (legacy top-level edges.parquet if present, else the first). Falls back - // gracefully to a single implicit "edges.parquet" if the endpoint returns nothing. - useEffect(() => { - if (!dataset) return; - fetch(`${apiBase}/edges/${dataset}/files`) - .then((r) => (r.ok ? r.json() : null)) - .then((data) => { - const files = Array.isArray(data?.files) ? data.files : []; - setEdgeFiles(files); - if (files.length > 0) { - const cur = useStore.getState().edgeFile; - const ids = files.map((f) => f.id); - if (!ids.includes(cur)) setEdgeFile(data.default ?? ids[0]); - } - }) - .catch(() => setEdgeFiles([])); - }, [apiBase, dataset]); // eslint-disable-line react-hooks/exhaustive-deps - - const efParam = `?edge_file=${encodeURIComponent(edgeFile)}`; - useEffect(() => { - if (!dataset) return; - fetch(`${apiBase}/edges/${dataset}/schema${efParam}`) - .then((r) => (r.ok ? r.json() : null)) - .then(setEdgeSchema) - .catch(() => {}); - if (lrmCatalogue.length === 0) { - fetch(`${apiBase}/edges/${dataset}/lrm-catalogue${efParam}`) - .then((r) => (r.ok ? r.json() : [])) - .then(setLrmCatalogue) - .catch(() => {}); + // The edge-file picker lives in each panel's header now, beside its dataset — + // edge sources are dataset-specific, so a single sidebar picker has no + // meaning once two panels can show two datasets. + const sources = active.filter((p) => p.dataset) + .map((p) => ({ dataset: p.dataset, ef: `?edge_file=${encodeURIComponent(p.edgeFile)}` })); + const srcKey = sources.map((x) => x.dataset + x.ef).join(" "); + + // LRM catalogue: the union of what each panel loaded. hiddenLrms is shared and + // keyed on the "ligand|receptor" string, so a mechanism present in both + // datasets is one checkbox governing both — which is the point of the + // comparison. Mechanisms unique to one panel simply do nothing in the other. + const lrmCatalogue = React.useMemo(() => { + const seen = new Set(), out = []; + for (const p of active) for (const e of p.lrmCatalogue ?? []) { + const id = e.lrm ?? `${e.ligand}|${e.receptor}`; + if (!seen.has(id)) { seen.add(id); out.push(e); } } - }, [apiBase, dataset, efParam]); // eslint-disable-line react-hooks/exhaustive-deps + return out; + }, [active]); + + // Edge metadata columns, unioned across panels. + const [edgeColumns, setEdgeColumns] = useState([]); + const [edgeDtypes, setEdgeDtypes] = useState({}); + useEffect(() => { + if (!sources.length) { setEdgeColumns([]); setEdgeDtypes({}); return; } + let cancelled = false; + Promise.all(sources.map(({ dataset, ef }) => + fetch(`${apiBase}/edges/${dataset}/schema${ef}`) + .then((r) => (r.ok ? r.json() : null)).catch(() => null))) + .then((rs) => { + if (cancelled) return; + const seen = new Set(), cols = [], dtypes = {}; + for (const r of [...rs].reverse()) Object.assign(dtypes, r?.columns ?? {}); + for (const r of rs) for (const c of Object.keys(r?.columns ?? {})) + if (!EDGE_SKIP_COLS.has(c) && !seen.has(c)) { seen.add(c); cols.push(c); } + setEdgeColumns(cols); setEdgeDtypes(dtypes); + }); + return () => { cancelled = true; }; + }, [apiBase, srcKey]); // eslint-disable-line react-hooks/exhaustive-deps + + // Legend range from panel 0; the colour scale is shared across panels. + const edgeColorRange = useStore((s) => s.panels[0].edgeColorRange); const handleStrength = (e) => { const v = parseFloat(e.target.value); @@ -1332,10 +1360,7 @@ function EdgeSection() { commitTimer.current = setTimeout(() => setEdgeMinStrength(v), 300); }; - // Metadata columns for color-by (exclude spatial/identity cols) - const metaCols = edgeSchema?.columns - ? Object.keys(edgeSchema.columns).filter((c) => !EDGE_SKIP_COLS.has(c)) - : []; + const metaCols = edgeColumns; const { mode, field } = edgeColorBy; const selectedLrmCount = lrmCatalogue.length - hiddenLrms.size; @@ -1344,7 +1369,7 @@ function EdgeSection() { // than reading the dtype matters for the same reason it does on the cell side: // the auto-rule also calls a low-cardinality integer column categorical, and an // explicit override can flip either way (issue #35). - const fieldDtype = field && edgeSchema ? edgeSchema.columns[field] : null; + const fieldDtype = field ? edgeDtypes[field] : null; const isNumericField = !!fieldDtype && /^(int|uint|float|double|Int|UInt|Float)/.test(fieldDtype); const edgeOverrideKey = `edge::${field}`; const edgeOverride = categoricalOverrides[edgeOverrideKey] ?? null; @@ -1353,37 +1378,20 @@ function EdgeSection() { useEffect(() => { if (mode !== "metadata" || !field) { setEdgeMeta(null); return; } let cancelled = false; - fetch(`${apiBase}/edges/${dataset}/edge-color-values${efParam}`, { - method: "POST", - headers: { "Content-Type": "application/json" }, - body: JSON.stringify({ mode: "metadata", field, categorical: edgeOverride }), - }) - .then((r) => (r.ok ? r.json() : null)) - .then((d) => { if (!cancelled && d) setEdgeMeta({ type: d.type, categories: d.categories ?? [] }); }) - .catch(() => {}); + mergeColorValues(sources.map(({ dataset, ef }) => + fetch(`${apiBase}/edges/${dataset}/edge-color-values${ef}`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ mode: "metadata", field, categorical: edgeOverride }), + }).then((r) => (r.ok ? r.json() : null)).catch(() => null))) + .then((d) => { if (!cancelled && d) setEdgeMeta({ type: d.type, categories: d.categories ?? [] }); }); return () => { cancelled = true; }; - }, [apiBase, dataset, efParam, mode, field, edgeOverride]); + }, [apiBase, srcKey, mode, field, edgeOverride]); // eslint-disable-line const isCategorical = mode === "metadata" && !!field && edgeMeta?.type === "categorical"; return (
      - {/* Edge-source picker (issue #46) — only shown when the dataset has more than - one edge file. Applies to every open viewer panel. */} - {edgeFiles.length > 1 && ( -
      - -
      - )} s.panels); if (regions.length === 0) return null; const exportRegion = async (region) => { + const dataset = panels[region.panelIndex ?? 0]?.dataset; + if (!dataset) return; const res = await fetch(`${apiBase}/spatial/${dataset}/cells/export`, { method: "POST", headers: { "Content-Type": "application/json" }, diff --git a/frontend/src/components/Viewer.jsx b/frontend/src/components/Viewer.jsx index 7d9aaf4..7d56271 100644 --- a/frontend/src/components/Viewer.jsx +++ b/frontend/src/components/Viewer.jsx @@ -28,6 +28,7 @@ import { useEdges } from "../hooks/useEdges"; import { useEdgeColors } from "../hooks/useEdgeColors"; import { geneColor } from "../utils/geneColor"; import AnnotationToolbar from "./AnnotationToolbar"; +import { DatasetPicker } from "./DatasetPicker"; import EdgeInfoPanel from "./EdgeInfoPanel"; import RenderingStatus from "./RenderingStatus"; @@ -120,39 +121,54 @@ function ViewerPanel({ panelIndex }) { // Recomputed in syncDeckFromOSD on every viewport change and on rotation change. const [rotModelMatrix, setRotModelMatrix] = useState(null); + // ── Shared settings: one sidebar drives every panel ─────────────────────── const { - apiBase, dataset, activeImage, - imageSize, setImageSize, + apiBase, setViewport, setViewportActual, layers: layerState, - platformCapabilities, setPlatformCapabilities, cellColorEnabled, colorBy, cellColorPalette, categoryColorOverrides, - allGenes, selectedGenes, transcriptColorOverrides, - selectedCell, setSelectedCell, - edgeMinStrength, edgeDensity, edgeFile, + selectedGenes, transcriptColorOverrides, + edgeMinStrength, edgeDensity, edgeColorBy, edgeColorPalette, edgeDirectional, showAutocrine, edgeWidth, showArrowheads, arrowStyle, arrowheadScale, edgeOffset, autocrineRadius, autocrineLineWidth, - hiddenLrms, lrmCatalogue, - selectedEdge, setSelectedEdge, - setCellColorRange, setEdgeColorRange, - cellColorClamp, edgeColorClamp, setEdgeColorClamp, - categoricalOverrides, cellFilter, edgeFilter, setCellColorType, + hiddenLrms, + cellColorClamp, edgeColorClamp, setEdgeColorClamp, linkColorScale, + categoricalOverrides, cellFilter, edgeFilter, annotationMode, - pixelSize, setPixelSize, clearZoomMatch, activeRegion, addRegionPoint, cancelActiveRegion, commitRegion, regions, removeRegion, measurements, addMeasurement, clearAnnotations, panelCount, - transcriptFraction, setTranscriptStats, - cellBoundaryFraction, setCellBoundaryStats, + transcriptFraction, + cellBoundaryFraction, setLoadingKey, panelRotations, setPanelRotation, + selection, setSelectedCell, setSelectedEdge, + patchPanel, } = useStore(); + // ── This panel's dataset-bound state ────────────────────────────────────── + // Everything derived from *which dataset this panel shows*: image dimensions, + // pixel size, capabilities, gene panel, LRM vocabulary, value ranges. + const panel = useStore((s) => s.panels[panelIndex]); + const { + dataset, activeImage, imageSize, platformCapabilities, + pixelSize, edgeFile, lrmCatalogue, allGenes, + } = panel; + // Convenience: write one or more fields of *this* panel. + const patch = useCallback((p) => patchPanel(panelIndex, p), [patchPanel, panelIndex]); + + // The selection belongs to whichever panel produced it; only that panel + // highlights it, so a click in panel 1 does not light up panel 0. + const selectedCell = selection?.panelIndex === panelIndex && selection.kind === "cell" + ? selection.cell : null; + const selectedEdge = selection?.panelIndex === panelIndex && selection.kind === "edge" + ? selection.edge : null; + const panelRotation = panelRotations[panelIndex] ?? 0; // Per-panel viewport from store @@ -209,18 +225,51 @@ function ViewerPanel({ panelIndex }) { useEffect(() => { syncRef.current = syncDeckFromOSD; }, [syncDeckFromOSD]); useEffect(() => { deckViewStateRef.current = deckViewState; }, [deckViewState]); - // Fetch platform info + capabilities once per dataset, only from panel 0 + // Platform info + capabilities, per panel. This used to be panel-0-only + // because both panels showed the same dataset; now each panel needs its own — + // pixel_size drives measurement and zoom matching, and capabilities decide + // which layers this panel can serve at all. useEffect(() => { - if (panelIndex !== 0 || !dataset) return; + if (!dataset) return; + let cancelled = false; fetch(`${apiBase}/spatial/${dataset}/info`) .then((r) => r.ok ? r.json() : null) .then((info) => { - if (!info) return; - if (info.pixel_size) setPixelSize(parseFloat(info.pixel_size)); - if (info.capabilities) setPlatformCapabilities(info.capabilities); + if (cancelled || !info) return; + patch({ + ...(info.pixel_size ? { pixelSize: parseFloat(info.pixel_size) } : {}), + ...(info.capabilities ? { platformCapabilities: info.capabilities } : {}), + }); }) .catch(() => {}); - }, [apiBase, dataset, panelIndex, setPixelSize, setPlatformCapabilities]); + return () => { cancelled = true; }; + }, [apiBase, dataset, patch]); + + // Gene panel for this panel's dataset. Needed here, not just in the sidebar: + // gene-set colouring sums whatever genes this panel actually measures, so a + // shared list from the other panel would sum genes it does not have. + useEffect(() => { + if (!dataset) return; + let cancelled = false; + fetch(`${apiBase}/spatial/${dataset}/genes`) + .then((r) => (r.ok ? r.json() : [])) + .then((g) => { if (!cancelled && Array.isArray(g)) patch({ allGenes: g, genesLoaded: true }); }) + .catch(() => {}); + return () => { cancelled = true; }; + }, [apiBase, dataset, patch]); + + // LRM catalogue for this panel's edge source. Fetched here rather than in the + // sidebar because it is per (dataset, edge file): the sidebar's mechanism + // checklist is the union of what the panels loaded. + useEffect(() => { + if (!dataset) return; + let cancelled = false; + fetch(`${apiBase}/edges/${dataset}/lrm-catalogue?edge_file=${encodeURIComponent(edgeFile)}`) + .then((r) => (r.ok ? r.json() : [])) + .then((cat) => { if (!cancelled && Array.isArray(cat)) patch({ lrmCatalogue: cat }); }) + .catch(() => {}); + return () => { cancelled = true; }; + }, [apiBase, dataset, edgeFile, patch]); // ── OpenSeadragon init ──────────────────────────────────────────────────── const dziUrl = `${apiBase}/tiles/${dataset}/dzi/${activeImage}.dzi`; @@ -263,7 +312,7 @@ function ViewerPanel({ panelIndex }) { const src = viewer.world.getItemAt(0); if (src) { const sz = src.getContentSize(); - setImageSize(sz.x, sz.y); + patch({ imageSize: { w: sz.x, h: sz.y } }); setOsdOpenCount((c) => c + 1); // always triggers morphology opacity effect } }); @@ -324,12 +373,32 @@ function ViewerPanel({ panelIndex }) { const srcVp = useStore.getState().viewportActual[fromPanel]; if (!srcVp) return; + // Match PHYSICAL scale — microns across the viewport — not fraction of image. + // + // This used to divide both panels' widths by the local imgW, i.e. it matched + // "the same proportion of the picture". With one dataset in both panels that + // was the same thing. With two it is meaningless: 20% of a 6.5 mm Visium + // capture area and 20% of a 107 µm seqFISH ROI differ by a factor of 60, and + // the panels would look matched while being nothing of the sort. + // + // Converting through each panel's own µm/px makes "match" mean what a + // scalebar would: the same number of microns spans the same screen width. + const srcPanel = useStore.getState().panels[fromPanel]; + const srcPixelSize = srcPanel?.pixelSize || 1; + const srcImgW = srcPanel?.imageSize?.w; + if (!srcImgW) return; + + // Source extent in microns, then back into *my* image pixels, then into the + // OSD-normalised units fitBounds expects (OSD normalises by image width). + const srcMicronsW = (srcVp.xmax - srcVp.xmin) * srcPixelSize; + const srcMicronsH = (srcVp.ymax - srcVp.ymin) * srcPixelSize; + const myPixelSize = pixelSize || 1; const imgW = imageSize.w; - // Source visible dimensions in OSD-normalised units (OSD normalises by imgW) - const srcW = (srcVp.xmax - srcVp.xmin) / imgW; - const srcH = (srcVp.ymax - srcVp.ymin) / imgW; + const srcW = (srcMicronsW / myPixelSize) / imgW; + const srcH = (srcMicronsH / myPixelSize) / imgW; + if (!isFinite(srcW) || !isFinite(srcH) || srcW <= 0) return; - // Keep my current center, apply source zoom + // Keep my current center, adopt the source's physical scale const center = viewerRef.current.viewport.getCenter(true); const newBounds = new OpenSeadragon.Rect( center.x - srcW / 2, @@ -370,34 +439,30 @@ function ViewerPanel({ panelIndex }) { if (e.shiftKey) { const edgeInfo = deckRef.current.pickObject({ x, y, radius: 8, layerIds: ["edges-directed", "edges-autocrine", "tissue-graph"] }); if (edgeInfo?.object) { - setSelectedEdge(edgeInfo.object.edge); - setSelectedCell(null); + setSelectedEdge(edgeInfo.object.edge, panelIndex); return; } const cellInfo = deckRef.current.pickObject({ x, y, radius: 6, layerIds: ["cell-segments-fill"] }); if (cellInfo?.object) { - setSelectedCell(cellInfo.object); - setSelectedEdge(null); + setSelectedCell(cellInfo.object, panelIndex); } else { - setSelectedCell(null); - setSelectedEdge(null); + setSelectedCell(null, panelIndex); } return; } const cellInfo = deckRef.current.pickObject({ x, y, radius: 6, layerIds: ["cell-segments-fill"] }); if (cellInfo?.object) { - setSelectedCell(cellInfo.object); - setSelectedEdge(null); + setSelectedCell(cellInfo.object, panelIndex); return; } - setSelectedCell(null); + setSelectedCell(null, panelIndex); const edgeInfo = deckRef.current.pickObject({ x, y, radius: 8, layerIds: ["edges-directed", "edges-autocrine", "tissue-graph"] }); if (edgeInfo?.object) { - setSelectedEdge(edgeInfo.object.edge); + setSelectedEdge(edgeInfo.object.edge, panelIndex); } else { - setSelectedEdge(null); + setSelectedEdge(null, panelIndex); } }, [setSelectedCell, setSelectedEdge]); @@ -530,7 +595,7 @@ function ViewerPanel({ panelIndex }) { // Expose live shown/total counts to the LayerPanel via the store (panel 0 only). // Use visibleTranscripts.length so the stat always reflects the selected species. useEffect(() => { - if (panelIndex === 0) setTranscriptStats(visibleTranscripts.length, transcriptTotal); + patch({ transcriptStats: { shown: visibleTranscripts.length, total: transcriptTotal } }); }, [visibleTranscripts.length, transcriptTotal]); // eslint-disable-line const { @@ -545,7 +610,7 @@ function ViewerPanel({ panelIndex }) { // Expose live cell boundary counts to LayerPanel (panel 0 only). useEffect(() => { - if (panelIndex === 0) setCellBoundaryStats(cellPolygons.length, cellBoundaryTotal); + patch({ cellBoundaryStats: { shown: cellPolygons.length, total: cellBoundaryTotal } }); }, [cellPolygons.length, cellBoundaryTotal]); // eslint-disable-line const { edges, loading: edgesLoading } = useEdges( @@ -559,23 +624,44 @@ function ViewerPanel({ panelIndex }) { const cellCategorical = categoricalOverrides[`cell::${colorBy?.field}`] ?? null; const edgeCategorical = categoricalOverrides[`edge::${edgeColorBy?.field}`] ?? null; + // ── Shared colour scale ─────────────────────────────────────────────────── + // With two panels showing two datasets, letting each auto-range to its own + // data produces two viridis pictures that look comparable and are not. When + // linked (the default) both panels clamp to the union of the two ranges, so + // the single legend is true for everything on screen. An explicit clamp from + // the sliders always wins — the user asked for that range specifically. + const allPanels = useStore((s) => s.panels); + const linkedRange = useMemo(() => { + if (!linkColorScale || panelCount < 2) return null; + const rs = allPanels.slice(0, panelCount) + .map((p) => p.cellColorRange).filter((r) => r && r.vmin != null && r.vmax != null); + if (rs.length < 2) return null; + return { lo: Math.min(...rs.map((r) => r.vmin)), hi: Math.max(...rs.map((r) => r.vmax)) }; + }, [allPanels, panelCount, linkColorScale]); + + const effectiveCellClamp = useMemo(() => ( + linkedRange + ? { low: cellColorClamp?.low ?? linkedRange.lo, high: cellColorClamp?.high ?? linkedRange.hi } + : cellColorClamp + ), [linkedRange, cellColorClamp]); + const { colorValues, vmin: cellVmin, vmax: cellVmax, type: cellType, categories: cellCategories, loading: cellColorsLoading, } = useCellColors( apiBase, dataset, colorBy, allGenes, selectedGenes, cellColorPalette, - cellColorEnabled, cellColorClamp, categoryColorOverrides, cellCategorical + cellColorEnabled, effectiveCellClamp, categoryColorOverrides, cellCategorical ); // Only update shared store ranges from panel 0 to avoid redundant updates useEffect(() => { - if (panelIndex === 0) setCellColorRange(cellVmin, cellVmax); + patch({ cellColorRange: { vmin: cellVmin, vmax: cellVmax } }); }, [cellVmin, cellVmax]); // eslint-disable-line // The backend is the authority on whether a column is categorical, so report // the type it actually returned rather than letting the panel re-derive it // from the schema dtype — the two disagreed for low-cardinality integers. useEffect(() => { - if (panelIndex === 0) setCellColorType(cellType, cellCategories); + patch({ cellColorType: cellType, cellColorCategories: cellCategories ?? [] }); }, [cellType, cellCategories]); // eslint-disable-line const edgeColorEnabled = edgeColorBy.mode !== "default"; @@ -584,7 +670,7 @@ function ViewerPanel({ panelIndex }) { edgeColorEnabled, edgeColorClamp, edges, edgeFile, edgeCategorical ); useEffect(() => { - if (panelIndex === 0) setEdgeColorRange(edgeVmin, edgeVmax); + patch({ edgeColorRange: { vmin: edgeVmin, vmax: edgeVmax } }); }, [edgeVmin, edgeVmax]); // eslint-disable-line useEffect(() => { @@ -593,7 +679,7 @@ function ViewerPanel({ panelIndex }) { } }, [edgeP95]); // eslint-disable-line - useEffect(() => { setSelectedEdge(null); }, [dataset]); // eslint-disable-line + useEffect(() => { setSelectedEdge(null, panelIndex); }, [dataset]); // eslint-disable-line // ── Loading state → store (for RenderingStatus badge) ──────────────────── // Aggregate all hook loading booleans into a single per-panel key so the @@ -1037,34 +1123,51 @@ function ViewerPanel({ panelIndex }) { {/* Loading / computing badge — appears ~400ms after any fetch starts */} - {/* Edge info panel — only rendered in panel 0 to avoid duplication */} - {panelIndex === 0 && selectedEdge && ( + {/* Edge info panel — rendered by the panel that owns the selection, so it + resolves against that panel's dataset and edge file. It used to be + pinned to panel 0, which was fine when both panels shared a dataset and + would show the wrong dataset's edge now. `selectedEdge` is already + null unless this panel made the selection, so this cannot duplicate. */} + {selectedEdge && ( setSelectedEdge(null)} + onClose={() => setSelectedEdge(null, panelIndex)} /> )} - {/* Dataset / panel / transcript label */} + {/* Panel header. In split mode this carries the panel's own dataset / + image / edge-source pickers, because those are the things that cannot + be shared once two panels can show two datasets. In single mode it + stays a passive label and the pickers live in the sidebar, exactly + where they have always been. */}
      = 2 ? "auto" : "none", + display: "flex", alignItems: "center", gap: 6, maxWidth: "calc(100% - 16px)", }} > - {dataset}{activeImage && activeImage !== BLANK_IMAGE_NAME ? ` / ${activeImage}` : ""} - {panelCount >= 2 && ( - · panel {panelIndex + 1} + {panelCount >= 2 ? ( + <> + {panelIndex + 1} + + + ) : ( + + {dataset} + {activeImage && activeImage !== BLANK_IMAGE_NAME ? ` / ${activeImage}` : ""} + )} {transcripts.length > 0 && ( - - {transcripts.length} transcripts - + {transcripts.length} tx )}
      @@ -1075,9 +1178,13 @@ function ViewerPanel({ panelIndex }) { // Thin wrapper: renders one or two ViewerPanels side by side. export default function Viewer() { - const { panelCount, dataset } = useStore(); + const panelCount = useStore((s) => s.panelCount); + // Panel 0 gates the placeholder: it is the one DatasetPicker initialises first, + // and a second panel still resolving its own dataset should not blank the one + // that is already drawing. + const panel0Dataset = useStore((s) => s.panels[0].dataset); - if (!dataset) { + if (!panel0Dataset) { return (
      diff --git a/frontend/src/hooks/usePanels.js b/frontend/src/hooks/usePanels.js new file mode 100644 index 0000000000000000000000000000000000000000..d1ed589fa6eb5e653222020fcdbe95b21e6734d9 GIT binary patch literal 3418 zcmbVP+iu)85bd+SVzMul7Q0%fk2W?znzU$v77dc7C}0>dSK_W1Q=~vr-Yg6Q{fK^H zzochKYA=c1hZ-;}QNy{MnK?6FOs8iwrJtnkrSr75js$tt$dzyuS`nym>P~v$P;!K%)r4%iw3^EeW#=tuhu%v(f`kUQT21j1jfuqM6wSXOfC0{r^pue^5mR(*$4yw0O zYa}4%fRUQfwpDeT1BS4dI6<`R;I_4ZrMEBy0VwWz;n>(4DbHb~JHP)M0XYa_fEI3K=D;YT@|+LT8KVG-fFl4g44! zmjR+dAij1I?iSApmG@ma11cF<>i}ZKzRAzgxAkCv74E>VS9rRUZ@CJ6QPTYxF>XGj zyRYqlke4hNy($V{(((oQD!LFqmzSq^{L}J2$~qYa7mYJ01*}423aRp1)v`FB9hpZc zSLy5V=`~b6Bt+iiE_e)&8LD;O3}~tvu%qBnc4p9ez6M(VjmfC?1Qle93ojya3LT^H zMPxW3O5t5qRb}Ls-pf!V9aSg#qUclbRqo`nS}U&a;(LpV6Q(S$XHm;lx^#_~ec)XB zuSG9b3Y4LkS)|*XG&S8RuU1^TF~1|tWaXX&>g>07bJ<*gtF+jm*HGS(FrX)vKRO4F zpdR*Qm54|>z<@?(Cm7Vn`}osoYc7==xAEA>K$BbYv8wB}2pCk|vFWc$kPDlb&?;d(Hp-Rv$XxB=YCr$~YUNvS^gV}m51cv!%@spoTEPOyXb!Gx;l1UT^&@@%a>U>S}y4uL(2Yq85mcoue|MK zQH=UdWr&q>{AKE4`Wl8ObR0qTsE%XIp7P@$^R*v`xG&K5K=vf9&*^18y*j@pfX?ER zF@_BN>9rU1JbjP3cmQK(zB(bR4_xM;7c>?I@jP=LX$6uvY$8vP*3DXmy8Y3*V&uvj z>X=@f^j3q6u-srn63&So6_|lc^I(s6(hnNk!d!b3v!OF!vxGFo%S*`qD(8qBV#&3_X~h3Mi_Xq#m2t6J|5c{oIHeI#c}l}56B{p z-!$HD`t-+Z2;;CPxxioEKmr1KYh$GcRs01x#Wap~H%khj^kLri#7dFX&=a z7gc1|xTMs&ybfajW6A}$FH?7?UVMvXNqJd;rVpxPD>t!~$M3lo_8=8aSyq_*b(=km zMNyWyrz<4M6a{W^i9}5)1g4(Ec-ftD^flic$u(BZ5;G=^cI+y=HG_9DiLfI6SD;9! zJFfn{xr41_?b$0X-b(rSw{T?{cUWZxnP6aTC;IN9zB2myjD4vVq8{IPr7^{A>DVb$T%M2*m5K@uyS p3G+sIflXwN7yb_f^e}I&z4Z(Exy5Ydt!mD has_transcripts, unit_label, … + pixelSize: 1.0, // µm per image pixel; drives measurement + zoom match + edgeFile: "edges.parquet", + lrmCatalogue: [], + allGenes: [], + genesLoaded: false, + cellColorRange: { vmin: null, vmax: null }, + edgeColorRange: { vmin: null, vmax: null }, + cellColorType: "continuous", + cellColorCategories: [], + transcriptStats: { shown: 0, total: 0 }, + cellBoundaryStats: { shown: 0, total: 0 }, + }; +} + export const useStore = create((set, get) => ({ - // ── Dataset ──────────────────────────────────────────────────────────────── apiBase: API, - dataset: null, // initialized from /spatial/datasets on first load - activeImage: "morphology", // which OME-TIFF is loaded as the background - - // edgeFile: which edge-source parquet the app renders. "edges.parquet" is the - // legacy top-level default; multiple sets live under the dataset's edges/ folder - // and are identified as "edges/.parquet" (see issue #46). Applies to all - // open viewer panels — the single sidebar drives every panel equally. - edgeFile: "edges.parquet", - - // Switching datasets resets all edge-file-scoped state so a stale LRM catalogue, - // filter, selection, or color range from the previous dataset never leaks through. - // activeImage is cleared too: image names are platform-specific ("morphology" on - // Xenium, "Roi1_DAPI" on seqFISH), and DatasetPicker only learns the new dataset's - // image list asynchronously. Without this, OSD spends that window requesting the - // previous dataset's image from the new one and logging 404s. - setDataset: (dataset) => set({ - dataset, activeImage: null, - selectedGenes: null, allGenes: [], genesLoaded: false, - platformCapabilities: null, categoryColorOverrides: {}, transcriptColorOverrides: {}, - edgeFile: "edges.parquet", lrmCatalogue: [], hiddenLrms: new Set(), - selectedEdge: null, edgeColorRange: { vmin: null, vmax: null }, - edgeColorClamp: { low: null, high: null }, - // Column names are dataset-specific, so a categorical override or an active - // filter naming a column the new dataset does not have would either do nothing - // or 400 on every viewport change. - categoricalOverrides: {}, cellFilter: null, edgeFilter: null, + + // ══ Per-panel state ═══════════════════════════════════════════════════════ + // + // Everything here is bound to *a dataset*, so with two panels showing two + // datasets it cannot be global. Style and choice settings (layer opacity, + // palettes, filters, colour-by) stay global for now: one sidebar drives both + // panels, which is what makes a side-by-side comparison comparable. Phase 2 + // splits those per panel behind sidebar tabs. + // + // Read as `panels[panelIndex]`. Panel 1 exists even in single mode so nothing + // has to guard on panelCount. + panels: [makePanel(), makePanel()], + + // Generic shallow patch. Specific transitions that must reset dependent state + // (setPanelDataset, setPanelEdgeFile) are separate below. + patchPanel: (i, patch) => set((s) => { + const next = [...s.panels]; + next[i] = { ...next[i], ...patch }; + return { panels: next }; + }), + + // Switching a panel's dataset resets everything derived from the old one. + // activeImage is cleared because image names are platform-specific + // ("morphology" on Xenium, "Roi1_DAPI" on seqFISH), and the picker only learns + // the new list asynchronously — without this, OSD spends that window asking + // the new dataset for the old dataset's image and logging 404s. + setPanelDataset: (i, dataset) => set((s) => { + const next = [...s.panels]; + next[i] = { ...makePanel(), dataset }; + // Selections belong to a dataset; drop any that pointed at the old one. + const sel = s.selection && s.selection.panelIndex === i ? null : s.selection; + return { + panels: next, + selection: sel, + // The shared settings that name a *column, gene or mechanism* are reset by + // any panel's dataset change, even though they are shared. They have to be: + // a filter naming a column the new dataset lacks 400s on every viewport + // change, and a gene allowlist from a different panel is meaningless. + // + // The cost is that switching one panel's dataset clears the other panel's + // filter too. That is the honest consequence of one sidebar driving both, + // and it goes away in Phase 2 when these become per-panel. + selectedGenes: null, + hiddenLrms: new Set(), + categoricalOverrides: {}, + cellFilter: null, + edgeFilter: null, + categoryColorOverrides: {}, + transcriptColorOverrides: {}, + colorBy: { mode: "off", field: null }, + cellColorClamp: { low: null, high: null }, + edgeColorClamp: { low: null, high: null }, + }; }), - setActiveImage: (activeImage) => set({ activeImage }), - - // Switching the edge file resets the same edge-scoped state: the LRM catalogue, - // hidden-LRM filter, current selection, and auto-computed color range/clamp are - // all specific to a given edges.parquet and must be re-derived for the new file. - setEdgeFile: (edgeFile) => set({ - edgeFile, lrmCatalogue: [], hiddenLrms: new Set(), selectedEdge: null, - edgeColorRange: { vmin: null, vmax: null }, edgeColorClamp: { low: null, high: null }, - // The edge filter names a column of the edge table, which differs between - // edge sources; the cell filter is unaffected because cells are shared. - edgeFilter: null, + + // The LRM catalogue, colour range and edge filter are all specific to one + // edges.parquet and must be re-derived when the source changes. + setPanelEdgeFile: (i, edgeFile) => set((s) => { + const next = [...s.panels]; + next[i] = { + ...next[i], edgeFile, lrmCatalogue: [], + edgeColorRange: { vmin: null, vmax: null }, + }; + const sel = s.selection && s.selection.panelIndex === i && s.selection.kind === "edge" + ? null : s.selection; + return { panels: next, selection: sel, hiddenLrms: new Set(), edgeFilter: null }; }), + // ── Selection ───────────────────────────────────────────────────────────── + // Carries the panel it came from, so the info panels know which dataset to + // query. Without that they would resolve a panel-1 click against panel 0's + // dataset and show the wrong cell. + // { panelIndex, kind: "cell" | "edge", cell? , edge? } + selection: null, + setSelectedCell: (cell, panelIndex = 0) => + set({ selection: cell ? { panelIndex, kind: "cell", cell } : null }), + setSelectedEdge: (edge, panelIndex = 0) => + set({ selection: edge ? { panelIndex, kind: "edge", edge } : null }), + clearSelection: () => set({ selection: null }), + // ── Categorical / continuous override (issue #35) ───────────────────────── // Keyed "cell::" / "edge::" → true | false. Absent means // auto-detect, which is what the backend does when `categorical` is null. @@ -66,30 +134,25 @@ export const useStore = create((set, get) => ({ // // cellFilter also governs edges: an edge is drawn only when BOTH endpoints // survive it. edgeFilter is independent and applies to the edge table itself. + + // ── Shared colour scale across panels ───────────────────────────────────── + // On by default, and this is a figure-integrity setting rather than a + // preference: two viridis panels that each auto-ranged to their own data look + // comparable and are not. Panel A's yellow might be 40 counts and panel B's + // 4,000. With this on, both panels map through one range computed across both, + // so the single legend describes everything on screen. + // + // Unlock it when one panel's range is so much narrower that shared scaling + // flattens it — then the panels are individually readable but not comparable, + // which is the trade you are making knowingly. + linkColorScale: true, + setLinkColorScale: (v) => set({ linkColorScale: v }), + cellFilter: null, setCellFilter: (f) => set({ cellFilter: f }), edgeFilter: null, setEdgeFilter: (f) => set({ edgeFilter: f }), - // Resolved type of the active cell color-by column, reported by panel 0 so the - // LayerPanel can render the matching legend. The panel used to guess from the - // schema dtype, which disagreed with the backend for low-cardinality integers: - // the canvas drew discrete colors while the panel showed a gradient with two - // sliders that did nothing. - cellColorType: "continuous", - cellColorCategories: [], - setCellColorType: (type, categories) => - set({ cellColorType: type, cellColorCategories: categories ?? [] }), - - // ── Platform capabilities (fetched from /spatial/{dataset}/info) ────────── - // null = not yet loaded; object = { has_morphology, has_transcripts, has_boundaries, unit_label } - platformCapabilities: null, - setPlatformCapabilities: (caps) => set({ platformCapabilities: caps }), - - // ── Image dimensions (from DZI descriptor, set when OSD opens) ─────────── - imageSize: { w: null, h: null }, - setImageSize: (w, h) => set({ imageSize: { w, h } }), - // ── Viewport (image pixel coords, kept in sync with OpenSeadragon) ──────── // One entry per panel; panel 1 is only used in split-screen mode. // viewports — expanded bbox used by data-fetching hooks (may be larger than @@ -147,15 +210,6 @@ export const useStore = create((set, get) => ({ setCellBoundaryFraction: (v) => set({ cellBoundaryFraction: v !== null ? Math.max(0.0001, Math.min(1.0, v)) : null, }), - // cellBoundaryStats: live sampled/total counts for the status display. - cellBoundaryStats: { shown: 0, total: 0 }, - setCellBoundaryStats: (shown, total) => set({ cellBoundaryStats: { shown, total } }), - - // ── Color range cache (updated from Viewer hooks for legend display) ────── - cellColorRange: { vmin: null, vmax: null }, - setCellColorRange: (vmin, vmax) => set({ cellColorRange: { vmin, vmax } }), - edgeColorRange: { vmin: null, vmax: null }, - setEdgeColorRange: (vmin, vmax) => set({ edgeColorRange: { vmin, vmax } }), // ── Color clamp / squish (oob::squish): values outside [low,high] map to palette ends) ── cellColorClamp: { low: null, high: null }, @@ -207,16 +261,12 @@ export const useStore = create((set, get) => ({ autocrineLineWidth: 2, setAutocrineLineWidth: (v) => set({ autocrineLineWidth: v }), - // Selected edge (for info panel): "SendingCell|ReceivingCell" string or null - selectedEdge: null, - setSelectedEdge: (edge) => set({ selectedEdge: edge }), - // ── LRM mechanism filter ─────────────────────────────────────────────────── - // hiddenLrms: Set of "ligand|receptor" string IDs to suppress. - // lrmCatalogue: [{lrm_id, lrm, ligand, receptor}] loaded once from the backend. + // Shared across panels and keyed on the "ligand|receptor" string, so a + // mechanism present in both datasets is one checkbox governing both — which is + // the point of a comparison. The catalogue it is checked against is per panel + // (panels[i].lrmCatalogue); the sidebar shows the union. hiddenLrms: new Set(), - lrmCatalogue: [], - setLrmCatalogue: (cat) => set({ lrmCatalogue: cat }), toggleLrm: (lrm) => set((s) => { const next = new Set(s.hiddenLrms); @@ -224,8 +274,15 @@ export const useStore = create((set, get) => ({ return { hiddenLrms: next }; }), setAllLrmsVisible: () => set({ hiddenLrms: new Set() }), + // The union across panels: hiddenLrms is shared, so "none" has to cover every + // mechanism visible in either panel or one side keeps drawing. hideAllLrms: () => - set((s) => ({ hiddenLrms: new Set(s.lrmCatalogue.map((e) => e.lrm ?? `${e.ligand}|${e.receptor}`)) })), + set((s) => ({ + hiddenLrms: new Set( + s.panels.flatMap((p) => p.lrmCatalogue) + .map((e) => e.lrm ?? `${e.ligand}|${e.receptor}`) + ), + })), setLayerProp: (id, prop, value) => set((s) => ({ layers: { ...s.layers, [id]: { ...s.layers[id], [prop]: value } }, @@ -243,18 +300,10 @@ export const useStore = create((set, get) => ({ cellColorPalette: "viridis", setCellColorPalette: (p) => set({ cellColorPalette: p }), - // allGenes: full gene panel, fetched once on dataset change - allGenes: [], - genesLoaded: false, - setAllGenes: (genes) => set({ allGenes: genes }), - setGenesLoaded: (loaded) => set({ genesLoaded: loaded }), - // transcriptFraction: fraction of viewport transcripts to request (0–1). // transcriptStats: live shown/total counts for the status display (panel 0). transcriptFraction: 0.1, setTranscriptFraction: (f) => set({ transcriptFraction: Math.max(0.0001, Math.min(1.0, f)) }), - transcriptStats: { shown: 0, total: 0 }, - setTranscriptStats: (shown, total) => set({ transcriptStats: { shown, total } }), // categoryColorOverrides: user-chosen colors for categorical metadata columns. // keyed by `${field}::${category}` → [r, g, b, 255]. Reset on dataset change. @@ -279,15 +328,7 @@ export const useStore = create((set, get) => ({ })), resetTranscriptColorOverrides: () => set({ transcriptColorOverrides: {} }), - // ── Selected cell ───────────────────────────────────────────────────────── - selectedCell: null, - setSelectedCell: (cell) => set({ selectedCell: cell }), - // ── Annotations ─────────────────────────────────────────────────────────── - // pixelSize: µm per image pixel, fetched from /spatial/{dataset}/info - pixelSize: 1.0, - setPixelSize: (v) => set({ pixelSize: v }), - // annotationMode: current interaction mode annotationMode: "pan", // "pan" | "region" | "measure" setAnnotationMode: (mode) => set({ annotationMode: mode }), From e668edaae13c4b69ae39cc8f8f0a5b645e9e1a76 Mon Sep 17 00:00:00 2001 From: Nuoya Wang Date: Wed, 12 Aug 2026 15:39:20 -0400 Subject: [PATCH 06/13] fix: support large JPEG2000 OME-TIFFs and add seqFISH per-cell counts MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two independent fixes surfaced while bringing up a full-size Spatial Genomics seqFISH dataset (91250x44667 DAPI, ~4 gigapixels). Tile pyramid — large JPEG2000 OME-TIFFs (pyramid.py, tiles.py, compose): libvips 8.16 ships OpenJPEG but still cannot decode this file's tiles (TIFF compression tag 34712) because it goes through libtiff, which has no codec for it; only tifffile+imagecodecs can. The tifffile fallback only builds levels up to MAX_TIFFFILE_DIM yet used to advertise the native size in the DZI, so the viewer requested deeper tiles that 404'd and rendered black on deep zoom. - Keep the descriptor at NATIVE size so overlay coordinates (cells, edges, transcripts, emitted as um / pixel_size) stay aligned with the image; a capped descriptor had misplaced every overlay by the scale factor. - Synthesise the un-built top levels on demand by upscaling the deepest built level (get_or_synth_tile), so deep zoom is smooth-blurry instead of black, with no large pre-build and bounded memory. - Log the previously-silent pyvips -> tifffile fallback so codec failures are visible. - Expose MAX_TIFFFILE_DIM as an env knob (default 16384) to trade RAM for a sharper deepest real level. seqFISH per-cell counts (seqfish_reader.py): Derive transcript_counts / total_counts from the CellxGene row sum so the cell-info panel shows real numbers instead of blanks (seqFISH v2 has no transcript->cell assignment; the row sum is the authoritative total). Also makes the counts available as a color-by / filter field. Existing datasets (Xenium, small images, the pyvips path) are unaffected: synthesis only engages above the deepest built level. --- .DS_Store | Bin 0 -> 6148 bytes backend/app/readers/seqfish_reader.py | 13 ++ backend/app/routers/tiles.py | 11 +- backend/app/tiling/pyramid.py | 171 ++++++++++++++++++++++---- docker-compose.yml | 5 + 5 files changed, 176 insertions(+), 24 deletions(-) create mode 100644 .DS_Store diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..39a6bc0ab468e95d0051c0ca7d6156da5b7273f3 GIT binary patch literal 6148 zcmeHKJ#SMn6g>_}8_}WJlA)E}FNl-@p-3HGLplf1wW#?j1(EbcN!Z`S%+i0rk6`S8 zM3+v`GQ!$U`~=RmO=9Qel?5S$d?ow5_+I-SAHQeE095PEumMy6lvxCe4_U1-@k_~A z!M5xWm5ebuZ5$4g@gx%&8iE2rfqzi}`F4w_;}owjcfVg%tGso6gIlbw8+hPH=FPA#z!pUZ(+vwaEu8?ocA%+?@@uCr+)PjPq@5Dmq+vS z;$6rY*X8#6Xpe98o$&orCNrXyw+x0lC=e6~3M>?m{UKrzj4h@H_13{kjz#eo1;zL* zcL~ABVr(%rNDob!u0+#S*(-)J-Ps=5xY%N9&~%5=nK_Qltn3X%>FjKebU0LOFw{YT zpn$Ky1M{;Y=l?Twxwq_ Optional[pd.DataFrame]: # microns (div == pixel_size), px² when it is in pixels (div == 1.0). ps = self.pixel_size out["cell_area"] = df["area"] * ((ps / div) ** 2) + + # Per-cell counts from CellxGene. seqFISH v2 ships no transcript→cell + # assignment, so unlike Xenium there is no molecule table to count; the + # authoritative per-cell total is the CellxGene row sum (total detected + # transcripts across all genes). Populate both fields the CellInfoPanel + # renders — otherwise "transcripts" / "total counts" sit blank on every + # seqFISH cell. Reuses the cached CellxGene frame, joined on cell_id. + cxg = self._cxg() + if cxg is not None: + totals = cxg.sum(axis=1) # Series indexed by cell_id + counts = out["cell_id"].map(totals).fillna(0).astype(int) + out["transcript_counts"] = counts + out["total_counts"] = counts return out def _cells_full(self) -> Optional[pd.DataFrame]: diff --git a/backend/app/routers/tiles.py b/backend/app/routers/tiles.py index e7825ec..076d084 100644 --- a/backend/app/routers/tiles.py +++ b/backend/app/routers/tiles.py @@ -11,7 +11,7 @@ from app.tiling.pyramid import ( BLANK_IMAGE_NAME, ensure_blank_pyramid, ensure_pyramid, - get_dzi_descriptor, get_tile_path, + get_dzi_descriptor, get_or_synth_tile, ) router = APIRouter() @@ -76,9 +76,14 @@ def dzi_descriptor(dataset: str, image_name: str): @router.get("/{dataset}/dzi/{image_name}_files/{level}/{col}_{row}.{fmt}") def tile(dataset: str, image_name: str, level: int, col: int, row: int, fmt: str): - """Return a single DZI tile.""" + """Return a single DZI tile. + + Tiles above the deepest built level (large JPEG2000 OME-TIFFs are only built + to the level that fits MAX_TIFFFILE_DIM) are synthesised on demand by + upscaling, so deep zoom stays smooth-blurry rather than going black. + """ dataset_path = DATA_ROOT / dataset - tile_path = get_tile_path(dataset_path, image_name, level, col, row, fmt) + tile_path = get_or_synth_tile(dataset_path, image_name, level, col, row, fmt) if tile_path is None or not tile_path.exists(): raise HTTPException(404, "Tile not found") return FileResponse(tile_path, media_type=f"image/{fmt}") diff --git a/backend/app/tiling/pyramid.py b/backend/app/tiling/pyramid.py index c4ca2c0..27c613e 100644 --- a/backend/app/tiling/pyramid.py +++ b/backend/app/tiling/pyramid.py @@ -138,6 +138,116 @@ def get_tile_path( return tile if tile.exists() else None +# Deepest-level base image held for on-demand tile synthesis, keyed by tiles_root. +# One at a time — each is tens of MB — so switching image releases the previous. +_SYNTH_BASE: dict[str, "Image.Image"] = {} + + +def _synth_base(tiles_root: Path, deepest: int, deep_w: int, deep_h: int): + """Stitch the deepest built DZI level into a single Pillow image, cached. + + Built from the already-written JPEG tiles (no OME re-decode), so it inherits + the same normalisation as the rest of the pyramid. + """ + key = f"{tiles_root}#{deepest}" + cached = _SYNTH_BASE.get(key) + if cached is not None: + return cached + level_dir = tiles_root / str(deepest) + if not level_dir.is_dir(): + return None + base = Image.new("L", (deep_w, deep_h)) + for f in level_dir.glob(f"*.{TILE_FORMAT}"): + try: + c, r = (int(v) for v in f.stem.split("_")) + except ValueError: + continue + x0 = max(0, c * TILE_SIZE - (OVERLAP if c > 0 else 0)) + y0 = max(0, r * TILE_SIZE - (OVERLAP if r > 0 else 0)) + with Image.open(f) as t: + base.paste(t.convert("L"), (x0, y0)) + _SYNTH_BASE.clear() # bound memory to a single base image + _SYNTH_BASE[key] = base + return base + + +def get_or_synth_tile( + dataset_path: Path, image_name: str, level: int, col: int, row: int, fmt: str +) -> Optional[Path]: + """Return a tile path, synthesising it when the requested level is above what + was built. + + Large JPEG2000 OME-TIFFs are only built down to the deepest level that fits + MAX_TIFFFILE_DIM; the descriptor still advertises native size (so overlays + align), so a client will request deeper levels that were never written. Rather + than 404 (which renders black), upscale the deepest built level for that tile. + Returns None when there is nothing to synthesise from (genuinely absent tile). + """ + existing = get_tile_path(dataset_path, image_name, level, col, row, fmt) + if existing is not None: + return existing + if image_name == BLANK_IMAGE_NAME: + return None + + tiles_root = _pyramid_root(dataset_path, image_name) / f"{image_name}_files" + if not tiles_root.is_dir(): + return None + built = sorted(int(d.name) for d in tiles_root.iterdir() + if d.is_dir() and d.name.isdigit()) + if not built: + return None + deepest = built[-1] + if level <= deepest: + return None # absent within the built range — not an upscale case + + try: + d = get_dzi_descriptor(dataset_path, image_name) + except FileNotFoundError: + return None + full_w, full_h = d["Size"]["Width"], d["Size"]["Height"] + max_dzi_level = math.ceil(math.log2(max(full_w, full_h))) + if level > max_dzi_level: + return None + + def _dims(lvl: int) -> tuple[int, int]: + return (max(1, math.ceil(full_w / 2 ** (max_dzi_level - lvl))), + max(1, math.ceil(full_h / 2 ** (max_dzi_level - lvl)))) + + level_w, level_h = _dims(level) + deep_w, deep_h = _dims(deepest) + + base = _synth_base(tiles_root, deepest, deep_w, deep_h) + if base is None: + return None + + # Target tile bounds in this level's pixel space — identical geometry to the + # builder, so a synthesised tile lines up with real ones at the boundary. + x0 = col * TILE_SIZE - (OVERLAP if col > 0 else 0) + y0 = row * TILE_SIZE - (OVERLAP if row > 0 else 0) + x1 = min(x0 + TILE_SIZE + OVERLAP * 2, level_w) + y1 = min(y0 + TILE_SIZE + OVERLAP * 2, level_h) + x0 = max(x0, 0) + y0 = max(y0, 0) + if x1 <= x0 or y1 <= y0: + return None + + # Map to deepest-level coordinates, crop, and upscale to the tile's size. + sx, sy = deep_w / level_w, deep_h / level_h + bx0, by0 = int(math.floor(x0 * sx)), int(math.floor(y0 * sy)) + bx1 = min(deep_w, max(bx0 + 1, int(math.ceil(x1 * sx)))) + by1 = min(deep_h, max(by0 + 1, int(math.ceil(y1 * sy)))) + crop = base.crop((bx0, by0, bx1, by1)).resize((x1 - x0, y1 - y0), Image.BILINEAR) + + level_dir = tiles_root / str(level) + level_dir.mkdir(parents=True, exist_ok=True) + out_path = level_dir / f"{col}_{row}.{fmt}" + try: + crop.save(out_path, quality=JPEG_QUALITY) + except Exception: + return None + return out_path + + # ────────────────────────────────────────────────────────────────────────────── # Build dispatcher # ────────────────────────────────────────────────────────────────────────────── @@ -151,8 +261,15 @@ def _build_dzi(src: Path, out_dir: Path, image_name: str) -> None: pyvips_started = True _build_dzi_pyvips(src, out_dir, image_name) return - except Exception: + except Exception as exc: # pyvips not installed, libvips missing, or can't process this file. + # Log the reason: this was silent before, which hid real failures such + # as JPEG2000-in-TIFF (compression tag 34712) that libvips cannot decode + # via libtiff even when built with OpenJPEG — the tifffile+imagecodecs + # fallback is what actually handles those files. + print(f"[pyramid] pyvips could not build {src.name} " + f"({'processing error' if pyvips_started else 'unavailable'}): " + f"{type(exc).__name__}: {exc} — falling back to tifffile/Pillow") # Clean up any partial output before falling back. if pyvips_started: for item in list(out_dir.iterdir()): @@ -246,50 +363,55 @@ def _build_dzi_pyvips(src: Path, out_dir: Path, image_name: str) -> None: # Backend 2 — tifffile + Pillow (pure Python fallback) # ────────────────────────────────────────────────────────────────────────────── +# Pixels; OME levels wider/taller than this are not loaded whole (a uint16 level +# at this size is ~0.5 GB, and normalisation transiently needs a few× that). The +# descriptor is always written at NATIVE size so overlay coordinates (which the +# readers emit as µm / pixel_size, i.e. native pixels) stay aligned with the +# tiles. Levels whose OME source exceeds this cap are simply left unbuilt and +# synthesised on demand by upscaling the deepest built level (get_or_synth_tile). +# Raise it (env override) on a box with spare RAM to build sharper top levels. +MAX_TIFFFILE_DIM = int(os.getenv("MAX_TIFFFILE_DIM", "16384")) + + def _build_dzi_tifffile(src: Path, out_dir: Path, image_name: str) -> None: """ Build DZI from OME-TIFF using tifffile + Pillow. - Reads one OME pyramid level at a time (rather than all at once) to keep - peak memory usage bounded. For images wider than MAX_TIFFFILE_DIM the - smallest OME level that fits within the limit is used as the top DZI level, - which is sufficient for laboratory-scale Xenium data. + Reads one OME pyramid level at a time (rather than all at once) to keep peak + memory usage bounded. The descriptor is written at the image's NATIVE size so + that cell/edge/transcript coordinates — which the readers emit in native pixel + space (µm / pixel_size) — line up with the tiles. OME levels wider than + MAX_TIFFFILE_DIM are too large to load whole, so the corresponding top DZI + levels are left unbuilt and produced on demand by upscaling the deepest built + level (see get_or_synth_tile). That keeps the image at native size while + bounding both build time and peak memory. """ import tifffile - MAX_TIFFFILE_DIM = 16384 # pixels; skip OME levels larger than this - with tifffile.TiffFile(src) as tif: series = tif.series[0] ax = series.axes.upper() - n_levels = len(series.levels) - full_shape = series.levels[0].shape - if ax.startswith(("Z", "C")): - full_h, full_w = full_shape[-2], full_shape[-1] - else: - full_h, full_w = full_shape[0], full_shape[1] + native_shape = series.levels[0].shape + full_w, full_h = native_shape[-1], native_shape[-2] # Compute normalization from the smallest OME level lo, hi = _tiff_norm_stats(series, ax) max_dzi_level = math.ceil(math.log2(max(full_w, full_h))) tiles_root = out_dir / f"{image_name}_files" + deepest_built = -1 for dzi_level in range(max_dzi_level + 1): level_w = max(1, math.ceil(full_w / 2 ** (max_dzi_level - dzi_level))) level_h = max(1, math.ceil(full_h / 2 ** (max_dzi_level - dzi_level))) - # Pick smallest OME level >= target, but skip if still too large ome_idx = _pick_ome_level_idx(series, ax, full_w, full_h, level_w, level_h) ome_lvl = series.levels[ome_idx] - ome_shape = ome_lvl.shape - ome_w = ome_shape[-1] - ome_h = ome_shape[-2] - - if max(ome_w, ome_h) > MAX_TIFFFILE_DIM: - # This OME level is too large to load safely; skip — lower DZI - # levels will be generated from smaller OME levels. + if max(ome_lvl.shape[-1], ome_lvl.shape[-2]) > MAX_TIFFFILE_DIM: + # Too large to load whole. Leave unbuilt — served on demand by + # upscaling the deepest built level, which keeps the image at + # native size so overlay coordinates stay aligned. continue arr = _read_ome_level_arr(ome_lvl, ax) @@ -320,6 +442,13 @@ def _build_dzi_tifffile(src: Path, out_dir: Path, image_name: str) -> None: tile.save(level_dir / f"{col}_{row}.{TILE_FORMAT}", quality=JPEG_QUALITY) del pil_img + deepest_built = dzi_level + + if deepest_built < max_dzi_level: + print(f"[pyramid] {src.name}: native {full_w}x{full_h}; DZI levels above " + f"{deepest_built} exceed MAX_TIFFFILE_DIM={MAX_TIFFFILE_DIM} and are " + f"upscaled on demand. Raise MAX_TIFFFILE_DIM (more RAM) for sharper " + f"top levels.") _write_dzi_xml(out_dir, image_name, full_w, full_h) diff --git a/docker-compose.yml b/docker-compose.yml index 8ecd07e..e70988d 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -26,6 +26,11 @@ services: # DuckDB memory cap — prevents a large parquet scan from consuming all # available RAM and making the container (and Docker Desktop VM) unresponsive. DUCKDB_MEMORY_LIMIT: ${DUCKDB_MEMORY_LIMIT:-8GB} + # Highest OME-TIFF level (in px) the tifffile builder loads whole. Only + # matters for JPEG2000 OME-TIFFs (Xenium, seqFISH DAPI) that libvips can't + # stream: the largest level within this limit becomes the deepest zoom. + # Raise it (needs proportionally more RAM) for sharper zoom on huge images. + MAX_TIFFFILE_DIM: ${MAX_TIFFFILE_DIM:-16384} ports: - "8000:8000" restart: unless-stopped From f4b2f2b876cfea208ed5132a15f23b942a68c855 Mon Sep 17 00:00:00 2001 From: Micha Sam Brickman Raredon <42100128+msraredon@users.noreply.github.com> Date: Thu, 13 Aug 2026 09:52:35 -0400 Subject: [PATCH 07/13] fix: clicking a gene in the picker unchecks it instead of selecting only it MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The gene picker renders every checkbox as ticked when `selectedGenes` is null (`checked = selectedGenes === null || selectedGenes.has(gene)`), but `toggleSelectedGene` treated a click in that state as "start an allowlist with just this gene". So the first click on a ticked box did the opposite of what the box said: it selected that one gene rather than excluding it. On a small demo panel that is merely surprising. On real data it does not look like a filter bug at all — on a 480-gene Xenium run one click took the transcript layer from 200,000 rendered dots to 363 over the same viewport, which presents as "transcripts are broken", with nothing in the UI obviously wrong beyond a "1 / 480 genes selected" line that reads as intentional. A click on a ticked box now excludes that gene, building the allowlist from the union of the visible panels' gene lists. Toggling the last gene back on collapses to null so "no filter" stays single-valued rather than becoming an explicit 480-name allowlist on every request. Behaviour predates v0.3.0 — `git tag --contains c52dbc0` puts it in v0.2.0. Verified on the reporting dataset (Rat-PPLR-Run1, 480 genes, 132M transcripts): clicking Abca3 now yields 479/480 with Abca3 excluded and 200,000 transcripts still rendering; clicking it again returns to no filter. Co-Authored-By: Claude Opus 5 --- CLAUDE.md | 15 ++++++++++++--- frontend/src/store.js | 23 +++++++++++++++++++++-- 2 files changed, 33 insertions(+), 5 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index 61c3dab..5882657 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -470,9 +470,18 @@ The selection is built from `allGenes` (fetched once per dataset from - **Expanded picker**: full gene list (searchable) with checkboxes, `all` (→ null) and `none` (→ empty Set) buttons -`toggleSelectedGene(gene)`: if `selectedGenes` is null, starts a new Set with just -that gene. If it's a Set, toggles membership. Opening the picker while null shows all -genes as checked; unchecking one starts an allowlist. +`toggleSelectedGene(gene)` **must agree with what the checkbox is showing**, and this +is the one thing to get right here. The picker renders `checked = selectedGenes === null +|| selectedGenes.has(gene)`, so in the null state *every box is ticked*. A click therefore +means **uncheck this one** — the action builds the allowlist "everything except this gene" +from the union of `panels[*].allGenes`. Toggling the last unchecked gene back on collapses +the Set to `null`, so "no filter" stays single-valued and hundreds of gene names stay out +of the request URL. + +It used to start an allowlist containing *only* the clicked gene — the exact inverse. The +symptom did not look like a filter bug: on a 480-gene Xenium panel one click took the +transcript layer from 200,000 dots to ~360, which reads as "transcripts stopped working". +Present since v0.2.0 (`c52dbc0`). `useCellColors` `gene_set` mode: if `selectedGenes === null`, uses all `allGenes`; otherwise uses `[...selectedGenes]`. diff --git a/frontend/src/store.js b/frontend/src/store.js index 47acf07..f96d80b 100644 --- a/frontend/src/store.js +++ b/frontend/src/store.js @@ -372,12 +372,31 @@ export const useStore = create((set, get) => ({ setSelectedGenes: (genes) => set({ selectedGenes: genes }), toggleSelectedGene: (gene) => set((s) => { + // The universe of genes is the union across the visible panels — the same + // list the picker renders. + const all = [...new Set( + s.panels.slice(0, s.panelCount).flatMap((p) => p.allGenes ?? []) + )]; + if (s.selectedGenes === null) { - // First selection from "show all" state: start an allowlist with just this gene. - return { selectedGenes: new Set([gene]) }; + // "Show all" renders EVERY checkbox ticked, so a click here means + // "uncheck this one" — exclude it and keep the rest. + // + // This used to start an allowlist containing only the clicked gene, the + // exact opposite of what the click meant. On a 480-gene Xenium panel + // that silently narrowed the transcript layer from 200,000 dots to ~360, + // which reads as "transcripts are broken" rather than "you filtered to + // one gene". The checkbox said checked; the click has to mean uncheck. + if (all.length === 0) return {}; // list not loaded yet — ignore + return { selectedGenes: new Set(all.filter((g) => g !== gene)) }; } + const next = new Set(s.selectedGenes); if (next.has(gene)) next.delete(gene); else next.add(gene); + // Back to everything selected is the same as no filter. Collapsing keeps + // the semantics single-valued and keeps hundreds of gene names out of the + // request URL. + if (all.length > 0 && next.size === all.length) return { selectedGenes: null }; return { selectedGenes: next }; }), })); From 6a7ffadd2cf5df5e67b2a7cb76b4e7e18e53443c Mon Sep 17 00:00:00 2001 From: Micha Sam Brickman Raredon <42100128+msraredon@users.noreply.github.com> Date: Thu, 13 Aug 2026 10:00:59 -0400 Subject: [PATCH 08/13] fix: report 0.8.3, the actual released version, not the unreleased 0.9.0 Both version strings claimed 0.9.0, but no 0.9.0 release exists anywhere. The v0.9.0 tag is local-only: `git ls-remote --tags` shows origin carries no tags at all, and upstream's newest is v.0.8.3. So the browser tab, the sidebar badge and GET /health all advertised a version nobody could obtain, and the badge's frontend-vs-backend mismatch check compared two equally wrong strings. The frontend reads frontend/package.json at build time via vite's __APP_VERSION__; the backend has its own constant. They have to be edited together or the badge turns red. Co-Authored-By: Claude Opus 5 --- backend/app/main.py | 2 +- frontend/package.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/backend/app/main.py b/backend/app/main.py index 701e166..124a35a 100644 --- a/backend/app/main.py +++ b/backend/app/main.py @@ -3,7 +3,7 @@ from app.routers import tiles, spatial, edges, layers -APP_VERSION = "0.9.0" +APP_VERSION = "0.8.3" app = FastAPI(title="TissuePlex API", version=APP_VERSION) diff --git a/frontend/package.json b/frontend/package.json index 28e6b20..76f4836 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -1,6 +1,6 @@ { "name": "tissueplex", - "version": "0.9.0", + "version": "0.8.3", "private": true, "scripts": { "dev": "vite", From ab9d73992792ebaff4c5ed653772dccd969cc20a Mon Sep 17 00:00:00 2001 From: Micha Sam Brickman Raredon <42100128+msraredon@users.noreply.github.com> Date: Thu, 13 Aug 2026 10:19:29 -0400 Subject: [PATCH 09/13] =?UTF-8?q?fix:=20transcripts=20never=20render=20on?= =?UTF-8?q?=20large=20datasets=20=E2=80=94=20OOM=20kill=20+=20blocking=20i?= =?UTF-8?q?ndex=20build?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Reported as "toggle transcripts on and nothing shows up" on a real Xenium run (132,789,299 transcripts, 1736 MB). The layer was empty because the request never returned data, for two independent reasons that compounded. 1. The backend was being killed. DUCKDB_MEMORY_LIMIT defaulted to 8GB in both compose files, but the Docker Desktop VM has 7.8 GB, so the spatial-index build's global ORDER BY over 132M rows ran the VM out of memory and the kernel killed uvicorn. This is invisible in the obvious places: the container limit is 12 GB so `OOMKilled` stays false and the exit code is 0 — only RestartCount climbing (5, here) shows it. It also cannot be caught by the build's own `except Exception`, since SIGKILL is not an exception. The limit is now derived from 60% of the minimum of the cgroup limit and physical RAM, and connect() sets a temp_directory under CACHE_DIR so DuckDB can spill — an in-memory DuckDB cannot go out-of-core without one, which is what made a sort this size impossible rather than merely slow. 2. The build blocked the request. Even surviving, it takes ~94 s against nginx's 120 s proxy_read_timeout. It now runs in a daemon thread while queries continue against the unsorted file, which answers them correctly, just slower. Either failure reached the frontend as a non-ok response, and the hooks return [] on non-ok — so the canvas showed nothing, exactly as if the platform served no transcripts. Small datasets are under the 64 MB cache threshold, which is why this never appeared on the bundled fixtures. Verified on the reporting dataset: 200,000 transcripts render across all sections, index builds in 94 s with 0 restarts, viewport query 2.3 s -> 0.37 s once adopted. Golden guard: 238 probes across 8 datasets, all matching. The baseline also absorbs the two seqFISH per-cell count columns added in e668eda, which was never re-recorded; verified by stashing this change that the drift is unrelated to it. Co-Authored-By: Claude Opus 5 --- CLAUDE.md | 23 ++++++++ backend/app/readers/duck.py | 78 +++++++++++++++++++++++++++- backend/app/readers/spatial_cache.py | 63 ++++++++++++++++++++-- backend/tests/golden_baseline.json | 16 ++++-- docker-compose.prod.yml | 4 +- docker-compose.yml | 12 +++-- 6 files changed, 184 insertions(+), 12 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index 5882657..ffd2ae6 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -1014,6 +1014,14 @@ Four things to know: uncached, so the golden baseline does not depend on whether a cache happens to exist. - **A failed build returns None and the query uses the source file**, so indexing can never make a dataset unreadable. `SPATIAL_CACHE=0` disables it entirely. +- **The build runs in a background thread; queries use the unsorted file until it lands.** + It used to build inline, which made the first transcript request on a real dataset + unservable: sorting 132M rows takes ~94 s against nginx's 120 s `proxy_read_timeout`, + and before the memory fix below it was killed outright. Either way the hook saw a + non-ok response, returned `[]`, and the layer rendered empty — indistinguishable from + "this platform has no transcripts". `_resolved` is left *unset* while a build is in + flight, because it is the decided-forever cache: writing None into it would pin the + source to the unsorted file for the life of the process even after the index landed. - **Cache validity covers the sort columns, not just the source stamp.** The cache filename derives from the source stem alone, so without that check a file sorted on one column pair would be served for a query on another — sorted by the wrong axis, silently. @@ -1229,6 +1237,21 @@ output folder under `DATA_PATH` — TissuePlex auto-detects the platform on firs Tuning knobs live in a `.env.prod` file that is gitignored and must be created by hand; `DUCKDB_MEMORY_LIMIT` is the one to reach for if the backend OOMs on large edge files. +**`DUCKDB_MEMORY_LIMIT` must not have a fixed default, and this is a real failure mode.** +It used to default to `8GB` in both compose files. On a stock Docker Desktop VM — 7.8 GB +here — that authorises DuckDB to take all of RAM, so the spatial-index build over a real +Xenium `transcripts.parquet` was killed by the *VM's* OOM killer mid-request, restarting +uvicorn. The container memory limit does not catch this: compose declares 12 GB, which is +larger than the VM, and `docker inspect` reports `OOMKilled=false` because the container +limit was never reached. Only `RestartCount` climbing gives it away. + +`duck.py::_default_memory_limit()` now takes 60% of the **minimum** of the cgroup limit and +physical RAM. Both numbers are needed — either can be the real ceiling and they routinely +disagree. `connect()` also sets `temp_directory` (under `CACHE_DIR`, the one writable +volume, since `/data` is mounted read-only): an in-memory DuckDB cannot spill without it, +so a sort larger than the cap fails outright instead of going out-of-core. Spilling is +what makes the 132M-row build possible at all rather than merely slower. + Other env knobs: `SPATIAL_CACHE=0` disables the spatial index entirely, `SPATIAL_CACHE_MIN_BYTES` (default 64 MB) sets the size below which files are left alone, and `CACHE_DIR` relocates both the DZI pyramids and the spatial index off the data volume. diff --git a/backend/app/readers/duck.py b/backend/app/readers/duck.py index 18fc7ca..7d7f2f6 100644 --- a/backend/app/readers/duck.py +++ b/backend/app/readers/duck.py @@ -12,15 +12,89 @@ """ import math import os +import tempfile from pathlib import Path +from typing import Optional import duckdb import pyarrow.parquet as pq -_MEMORY_LIMIT = os.getenv("DUCKDB_MEMORY_LIMIT", "8GB") +def _available_memory_bytes() -> Optional[int]: + """Memory this process may actually use, or None if it cannot be determined. + + Takes the **minimum** of the cgroup limit and physical RAM, because either + can be the real ceiling and they routinely disagree. On Docker Desktop the + container limit is whatever compose declares (12 GB here) while the Linux VM + hosting it may have far less (7.8 GB) — trusting the cgroup alone invites the + VM's OOM killer, which kills the process without the container ever reporting + OOMKilled. + """ + limits = [] + for p in ("/sys/fs/cgroup/memory.max", # cgroup v2 + "/sys/fs/cgroup/memory/memory.limit_in_bytes"): # cgroup v1 + try: + raw = Path(p).read_text().strip() + if raw and raw != "max": + v = int(raw) + # v1 reports a sentinel near 2^63 to mean "unlimited". + if 0 < v < (1 << 62): + limits.append(v) + except (OSError, ValueError): + pass + try: + limits.append(os.sysconf("SC_PAGE_SIZE") * os.sysconf("SC_PHYS_PAGES")) + except (OSError, ValueError, AttributeError): + pass + return min(limits) if limits else None + + +def _default_memory_limit() -> str: + """A memory cap that leaves room for everything else in the process. + + The old default was a flat ``8GB`` regardless of the machine. That is not a + cap at all on a standard 8 GB Docker Desktop VM — it authorises DuckDB to + take essentially all of RAM, and a large sort then dies to the OOM killer + mid-request, taking uvicorn with it. Sizing from what is actually present + keeps the same intent (bound the scan) while making the bound real. + + 60% leaves headroom for the Python process, pyvips tile builds, and the + page cache the parquet scan itself depends on. + """ + total = _available_memory_bytes() + if not total: + return "4GB" # unknowable → conservative, not greedy + mb = max(1024, int(total * 0.60 / (1024 * 1024))) + return f"{mb}MB" + + +_MEMORY_LIMIT = os.getenv("DUCKDB_MEMORY_LIMIT") or _default_memory_limit() _THREADS = os.getenv("DUCKDB_THREADS", "4") +def _temp_dir() -> Optional[str]: + """Writable scratch directory for DuckDB to spill to, or None. + + Without this an in-memory DuckDB cannot spill, so any operation whose working + set exceeds ``memory_limit`` fails outright instead of going out-of-core. The + spatial-index build sorts the entire transcripts file, which on a real Xenium + run is 132M rows — far past any sane cap — so spilling is what makes that + build possible at all rather than merely slower. + + CACHE_DIR is the right home: it is the one writable volume the backend owns + (/data is mounted read-only), and it already holds derived artifacts. + """ + base = os.getenv("CACHE_DIR") or tempfile.gettempdir() + try: + d = Path(base) / "duckdb-tmp" + d.mkdir(parents=True, exist_ok=True) + return str(d) + except OSError: + return None + + +_TEMP_DIR = _temp_dir() + + def connect() -> duckdb.DuckDBPyConnection: """Return a fresh, isolated DuckDB connection. @@ -31,6 +105,8 @@ def connect() -> duckdb.DuckDBPyConnection: conn = duckdb.connect() conn.execute(f"SET memory_limit='{_MEMORY_LIMIT}'") conn.execute(f"SET threads={_THREADS}") + if _TEMP_DIR: + conn.execute(f"SET temp_directory='{_TEMP_DIR}'") return conn diff --git a/backend/app/readers/spatial_cache.py b/backend/app/readers/spatial_cache.py index 5c963a1..f3d8a63 100644 --- a/backend/app/readers/spatial_cache.py +++ b/backend/app/readers/spatial_cache.py @@ -68,6 +68,9 @@ # Paths already resolved this process, to skip the stat/manifest check per query. _resolved: dict[str, Optional[Path]] = {} +# Sources with a build currently running in a background thread. +_building: set[str] = set() + def _lock_for(key: str) -> threading.Lock: with _locks_guard: @@ -146,9 +149,63 @@ def sorted_path( print(f"[spatial_cache] {source.name}: cache is stale " f"(source or sort columns changed); rebuilding") - built = _build(source, cache_file, manifest, stamp, x_col, y_col) - _resolved[key] = built - return built + # Build off the request thread and serve the source file meanwhile. + # + # The build sorts the whole file — on a real Xenium run that is 132M rows + # and minutes of work, against nginx's 120s proxy_read_timeout. Building + # inline meant the very first transcript request could not succeed: it + # either timed out or, before the memory fixes in duck.py, was killed + # outright. Either way the hook saw a non-ok response, returned [], and + # the layer rendered empty — indistinguishable from "this dataset has no + # transcripts", which is exactly how it was reported. + # + # The unsorted file answers the same query correctly, just slower, so + # there is no reason to make anyone wait for the index. Queries are + # served from the source until the build lands, then pick it up. + _start_background_build(key, source, cache_file, manifest, stamp, x_col, y_col) + return None + + +def _start_background_build( + key: str, + source: Path, + cache_file: Path, + manifest: Path, + stamp: dict, + x_col: str, + y_col: str, +) -> None: + """Kick off an index build in a daemon thread, at most one per source. + + `_resolved` is deliberately left unset while a build is in flight: it is the + "decided forever" cache, and writing None into it would pin this source to + the unsorted file for the life of the process even after the index landed. + Queries fall through to the source until the thread publishes a result. + """ + with _locks_guard: + if key in _building: + return + _building.add(key) + + def run() -> None: + try: + built = _build(source, cache_file, manifest, stamp, x_col, y_col) + # A failed build resolves to None on purpose — retrying a build that + # just failed on every subsequent viewport change would turn one bad + # file into a rebuild storm. + _resolved[key] = built + except Exception as exc: # _build catches its own, but never let a + print(f"[spatial_cache] background build for {source.name} " + f"failed: {exc}") # thread die silently + _resolved[key] = None + finally: + with _locks_guard: + _building.discard(key) + + print(f"[spatial_cache] {source.name}: building index in the background; " + f"queries use the unsorted file until it is ready") + threading.Thread(target=run, name=f"spatial-index-{source.name}", + daemon=True).start() def _build( diff --git a/backend/tests/golden_baseline.json b/backend/tests/golden_baseline.json index 4d7c5a7..87d87cb 100644 --- a/backend/tests/golden_baseline.json +++ b/backend/tests/golden_baseline.json @@ -902,16 +902,18 @@ "has_transcripts": true, "unit_label": "cell" }, - "cell_detail_0": "178c5b055b70bf72", + "cell_detail_0": "13ac6752da0385d8", "cell_expression_0": { "digest": "cb42bde899c5553c", "n": 12 }, "cells_all": { - "digest": "20ddf74e79b6dafb", + "digest": "f8ae7e72333ece28", "keys": [ "cell_area", "cell_id", + "total_counts", + "transcript_counts", "x_centroid", "y_centroid" ], @@ -920,6 +922,8 @@ "cells_schema": { "columns": { "cell_area": "float64", + "total_counts": "int64", + "transcript_counts": "int64", "x_centroid": "float64", "y_centroid": "float64" } @@ -1109,16 +1113,18 @@ "has_transcripts": true, "unit_label": "cell" }, - "cell_detail_0": "25ca1be475fb76eb", + "cell_detail_0": "6ad6a7c9da8b7d49", "cell_expression_0": { "digest": "a21358674d54a7f2", "n": 36 }, "cells_all": { - "digest": "3041ba131e9fb3b6", + "digest": "df0f5788b866d302", "keys": [ "cell_area", "cell_id", + "total_counts", + "transcript_counts", "x_centroid", "y_centroid" ], @@ -1127,6 +1133,8 @@ "cells_schema": { "columns": { "cell_area": "float64", + "total_counts": "int64", + "transcript_counts": "int64", "x_centroid": "float64", "y_centroid": "float64" } diff --git a/docker-compose.prod.yml b/docker-compose.prod.yml index f3498b0..a92f906 100644 --- a/docker-compose.prod.yml +++ b/docker-compose.prod.yml @@ -37,7 +37,9 @@ services: environment: DATA_ROOT: /data CACHE_DIR: /cache - DUCKDB_MEMORY_LIMIT: ${DUCKDB_MEMORY_LIMIT:-8GB} + # Unset → sized from available memory. See docker-compose.yml for why a + # fixed default is unsafe. Override in .env.prod if you need a tighter cap. + DUCKDB_MEMORY_LIMIT: ${DUCKDB_MEMORY_LIMIT:-} restart: unless-stopped stop_grace_period: 5m deploy: diff --git a/docker-compose.yml b/docker-compose.yml index e70988d..bce7c41 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -23,9 +23,15 @@ services: environment: DATA_ROOT: /data CACHE_DIR: /cache - # DuckDB memory cap — prevents a large parquet scan from consuming all - # available RAM and making the container (and Docker Desktop VM) unresponsive. - DUCKDB_MEMORY_LIMIT: ${DUCKDB_MEMORY_LIMIT:-8GB} + # DuckDB memory cap. Leave unset to size it from the memory actually + # available (60% of the lesser of the cgroup limit and the host's RAM). + # + # Do not put a fixed default back here. It used to read `:-8GB`, which on a + # stock 8 GB Docker Desktop VM authorised DuckDB to take all of RAM: the + # spatial-index build over a 132M-row transcripts file was then killed by + # the VM's OOM killer mid-request, restarting the backend. The container + # limit below does not catch that — it is larger than the VM. + DUCKDB_MEMORY_LIMIT: ${DUCKDB_MEMORY_LIMIT:-} # Highest OME-TIFF level (in px) the tifffile builder loads whole. Only # matters for JPEG2000 OME-TIFFs (Xenium, seqFISH DAPI) that libvips can't # stream: the largest level within this limit becomes the deepest zoom. From 73575ac1f8562a58009523eb73cf4e6da81e0344 Mon Sep 17 00:00:00 2001 From: Micha Sam Brickman Raredon <42100128+msraredon@users.noreply.github.com> Date: Thu, 13 Aug 2026 10:21:36 -0400 Subject: [PATCH 10/13] chore: untrack .DS_Store and ignore it at any depth MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit A .DS_Store was committed to the repo root in e668eda alongside unrelated tiling work. It is macOS Finder view metadata — per-machine, meaningless to anyone else, and rewritten whenever the folder is opened in Finder, so it shows up as spurious churn in every status and diff. `git rm --cached` untracks it without deleting it, so local Finder view settings survive. The pattern is left unanchored (`.DS_Store`, not `/.DS_Store`) so it also covers the copies already sitting in r/, docs/ and sample_data/, plus any directory browsed in future. Co-Authored-By: Claude Opus 5 --- .DS_Store | Bin 6148 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 .DS_Store diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 39a6bc0ab468e95d0051c0ca7d6156da5b7273f3..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKJ#SMn6g>_}8_}WJlA)E}FNl-@p-3HGLplf1wW#?j1(EbcN!Z`S%+i0rk6`S8 zM3+v`GQ!$U`~=RmO=9Qel?5S$d?ow5_+I-SAHQeE095PEumMy6lvxCe4_U1-@k_~A z!M5xWm5ebuZ5$4g@gx%&8iE2rfqzi}`F4w_;}owjcfVg%tGso6gIlbw8+hPH=FPA#z!pUZ(+vwaEu8?ocA%+?@@uCr+)PjPq@5Dmq+vS z;$6rY*X8#6Xpe98o$&orCNrXyw+x0lC=e6~3M>?m{UKrzj4h@H_13{kjz#eo1;zL* zcL~ABVr(%rNDob!u0+#S*(-)J-Ps=5xY%N9&~%5=nK_Qltn3X%>FjKebU0LOFw{YT zpn$Ky1M{;Y=l?Twxwq_ Date: Thu, 13 Aug 2026 10:50:39 -0400 Subject: [PATCH 11/13] fix: gene filter travels as allowlist or complement, whichever is shorter MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Review follow-up to f4b2f2b. That fix made "uncheck one gene" produce an allowlist of everything else, which is correct behaviour but put the whole panel into the URL of a GET. Measured against this stack: 479 of 480 genes is a 7,780-byte URL, ~220 bytes below nginx's 8 KB request-line limit, and 600 genes returns 414. So it worked on the reporting dataset by luck, and a Xenium Prime 5K panel would have failed on the first click. The complement is short exactly when the allowlist is long, so the client now sends whichever is smaller. `exclude_genes` is resolved back into an allowlist in the router against reader.gene_list(), leaving the reader interface — six implementations — with a single gene argument. Unchecking one gene now sends 157 bytes instead of 7,780. nginx's large_client_header_buffers goes to 64k to cover the worst case the rule can still produce, half a panel. Verified equivalent, not just shorter: exclude_genes=Abca3 and the explicit 479-gene allowlist both return total=34884 on the same viewport. Also in this pass: - An empty selectedGenes Set now short-circuits the fetch. It means "show no species", but omitting the parameter means "no filter", so the request returned the full 200K-row cap for Viewer's client-side filter to throw away: nothing drew, which looked correct, while ~20 MB moved per pan and the layer badge showed the unfiltered total against an empty canvas. - spatial_cache's _resolved memo is keyed on the sort columns, not just the path. The manifest check refuses a cache sorted on a different column pair, but the memo sat in front of it and would have returned that exact file on every later call. Not reachable today — each source is queried with one fixed pair — but it silently defeated the check written to prevent it. - Tidied a comment in _start_background_build that had been split mid-sentence across two lines. Golden guard: 238 probes across 8 datasets, all matching. Co-Authored-By: Claude Opus 5 --- .gitignore | 4 +++ CLAUDE.md | 22 +++++++++++++ backend/app/readers/spatial_cache.py | 17 +++++++--- backend/app/routers/spatial.py | 26 +++++++++++++-- frontend/nginx.conf | 8 +++++ frontend/src/components/Viewer.jsx | 2 +- frontend/src/hooks/useTranscripts.js | 48 +++++++++++++++++++++++++--- 7 files changed, 115 insertions(+), 12 deletions(-) diff --git a/.gitignore b/.gitignore index f1bde4b..010c171 100644 --- a/.gitignore +++ b/.gitignore @@ -61,3 +61,7 @@ backend/.dzi_cache/ .env.prod .claude/ + +# macOS Finder metadata — created in every directory browsed in Finder. +# Pattern is unanchored so it matches at any depth, not just the repo root. +.DS_Store diff --git a/CLAUDE.md b/CLAUDE.md index ffd2ae6..e8d1559 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -483,6 +483,28 @@ symptom did not look like a filter bug: on a 480-gene Xenium panel one click too transcript layer from 200,000 dots to ~360, which reads as "transcripts stopped working". Present since v0.2.0 (`c52dbc0`). +**The filter is sent as an allowlist or as its complement, whichever is shorter** +(`genes=` vs `exclude_genes=`). This is not an optimisation — it is what keeps the +feature working at all. The list travels in the query string of a GET, and measured +against this stack, 479 of 480 genes is a **7,780-byte URL, ~220 bytes under nginx's +8 KB request-line limit**, while 600 genes returned **414**. Deselecting a handful of +genes from a large panel is the ordinary case and produces exactly that shape, so a +Xenium Prime 5K run would have failed on the first click. `exclude_genes` is resolved +back to an allowlist in `spatial.py` against `reader.gene_list()`, so no reader sees +the inverted form. `nginx.conf` also raises `large_client_header_buffers` to 64k, which +covers the worst case the complement rule can still produce (half a panel). + +Note the two forms are equivalent to each other but **not** to sending no filter at all: +`gene_list()` omits controls and blanks, so any explicit selection drops them while +"no filter" keeps them (measured: 35,013 vs 34,997 rows in one viewport). That predates +this change and is why the picker never lists control probes. + +An **empty** `selectedGenes` Set short-circuits the fetch. It means "show no species", +but omitting the gene parameter means "no filter" to the backend, so the request used to +return the full 200K-row cap for Viewer's client-side filter to discard — nothing drew, +which looked right, while ~20 MB was fetched per pan and the layer badge reported the +unfiltered total against an empty canvas. + `useCellColors` `gene_set` mode: if `selectedGenes === null`, uses all `allGenes`; otherwise uses `[...selectedGenes]`. diff --git a/backend/app/readers/spatial_cache.py b/backend/app/readers/spatial_cache.py index f3d8a63..535b73e 100644 --- a/backend/app/readers/spatial_cache.py +++ b/backend/app/readers/spatial_cache.py @@ -109,7 +109,13 @@ def sorted_path( if not ENABLED or not source.exists(): return None - key = str(source.resolve()) + # Keyed on the sort columns as well as the path. The manifest check below + # already refuses a cache sorted on a different column pair, but this memo + # sits in front of it and would hand back that exact file on every later + # call — defeating the check written to prevent it. Not reachable today + # (each source is queried with one fixed column pair) but silent if it ever + # were: the data would be right and the row-group pruning wrong. + key = f"{source.resolve()}::{x_col}::{y_col}" if key in _resolved: return _resolved[key] @@ -194,9 +200,12 @@ def run() -> None: # just failed on every subsequent viewport change would turn one bad # file into a rebuild storm. _resolved[key] = built - except Exception as exc: # _build catches its own, but never let a - print(f"[spatial_cache] background build for {source.name} " - f"failed: {exc}") # thread die silently + except Exception as exc: + # _build already swallows its own errors, so reaching here means + # something unexpected. Catch it anyway: an exception escaping a + # thread is printed to stderr and then lost, leaving the key stuck + # in _building so no later request would ever retry the build. + print(f"[spatial_cache] background build for {source.name} failed: {exc}") _resolved[key] = None finally: with _locks_guard: diff --git a/backend/app/routers/spatial.py b/backend/app/routers/spatial.py index 0eed30f..6c53626 100644 --- a/backend/app/routers/spatial.py +++ b/backend/app/routers/spatial.py @@ -149,11 +149,33 @@ def transcripts( xmax: float = Query(None), ymax: float = Query(None), genes: list[str] = Query(None), + exclude_genes: list[str] = Query(None), fraction: float = Query(1.0), ): """Transcript records filtered by bounding box and/or gene list. - Returns {"transcripts": [...], "total": N} where total is the pre-sample count.""" - return _reader(dataset).transcripts( + + `genes` is an allowlist; `exclude_genes` is its complement, resolved here + against the dataset's gene list. Both express the same filter, and the client + sends whichever is shorter, because this is a GET and the list goes in the + URL: on a 480-gene panel "everything except one gene" is 7.8 KB of query + string, ~220 bytes under nginx's 8 KB request-line limit, and a 600-gene + panel 414s outright. Stated as an exclusion the same filter is one parameter. + + Resolving here rather than in the readers keeps `SpatialDatasetReader` to a + single gene argument — six readers implement it and none of them need to know + the filter arrived inverted. + + Returns {"transcripts": [...], "total": N} where total is the pre-sample count. + """ + reader = _reader(dataset) + if exclude_genes: + excluded = set(exclude_genes) + remaining = [g for g in reader.gene_list() if g not in excluded] + # An empty allowlist and "no filter" are different things, and `genes=None` + # means the latter. Excluding every gene must therefore yield no rows, not + # all of them, so fall back to a sentinel that matches nothing. + genes = (genes or []) + remaining if genes else (remaining or ["\0"]) + return reader.transcripts( bbox=(xmin, ymin, xmax, ymax) if xmin is not None else None, genes=genes, fraction=fraction, diff --git a/frontend/nginx.conf b/frontend/nginx.conf index 5b52f3f..2f8984b 100644 --- a/frontend/nginx.conf +++ b/frontend/nginx.conf @@ -3,6 +3,14 @@ server { root /usr/share/nginx/html; index index.html; + # Gene filters travel in the query string of a GET, and the default 8k + # request-line buffer is not enough: 479 of 480 genes measured 7,780 bytes, + # and 600 genes returned 414. The client now sends whichever of the + # allowlist / its complement is shorter, which bounds a request at about + # half the panel — 64k covers that for a 5,000-gene Xenium Prime run. + # Buffers are allocated only when a request actually needs them. + large_client_header_buffers 4 64k; + # Proxy API calls to backend location /api/ { proxy_pass http://backend:8000/; diff --git a/frontend/src/components/Viewer.jsx b/frontend/src/components/Viewer.jsx index 7d56271..1fbdb3d 100644 --- a/frontend/src/components/Viewer.jsx +++ b/frontend/src/components/Viewer.jsx @@ -583,7 +583,7 @@ function ViewerPanel({ panelIndex }) { const hasBoundaries = platformCapabilities?.has_boundaries ?? true; const { transcripts, total: transcriptTotal, loading: transcriptsLoading } = useTranscripts( - apiBase, dataset, viewport, imageSize, transcriptsVisible && hasTranscripts, transcriptFraction, selectedGenes + apiBase, dataset, viewport, imageSize, transcriptsVisible && hasTranscripts, transcriptFraction, selectedGenes, allGenes ); // visibleTranscripts: server already filtered by selectedGenes, so this is a no-op diff --git a/frontend/src/hooks/useTranscripts.js b/frontend/src/hooks/useTranscripts.js index fda23d0..a5c4edd 100644 --- a/frontend/src/hooks/useTranscripts.js +++ b/frontend/src/hooks/useTranscripts.js @@ -9,11 +9,13 @@ import { useState, useEffect, useRef } from "react"; * @param fraction 0–1 fraction of viewport transcripts to request. * @param selectedGenes null = all species; Set = only those genes. * Passed to the backend so total reflects selected species only. + * @param allGenes the dataset's full gene panel, used only to decide whether + * to state the filter as an allowlist or as its complement. * * Returns { transcripts, total, loading, error }. * total — pre-sample count in the viewport after gene filtering (from backend). */ -export function useTranscripts(apiBase, dataset, viewport, imageSize, enabled = true, fraction = 1.0, selectedGenes = null) { +export function useTranscripts(apiBase, dataset, viewport, imageSize, enabled = true, fraction = 1.0, selectedGenes = null, allGenes = null) { const [transcripts, setTranscripts] = useState([]); const [total, setTotal] = useState(0); const [loading, setLoading] = useState(false); @@ -32,6 +34,20 @@ export function useTranscripts(apiBase, dataset, viewport, imageSize, enabled = return; } + // An empty allowlist means "show no species" — distinct from null, which + // means "no filter". Without this the request omits the gene parameter + // entirely, so the backend applies no filter and returns the full 200K-row + // cap; Viewer's client-side filter then discards every row. Nothing draws, + // which looks right, but a ~20 MB response is fetched and thrown away on + // every pan and the layer's shown/total badge reports the unfiltered count + // while the canvas is empty. + if (selectedGenes !== null && selectedGenes.size === 0) { + setTranscripts([]); + setTotal(0); + setLoading(false); + return; + } + clearTimeout(timerRef.current); timerRef.current = setTimeout(async () => { // Cancel any in-flight request before starting a new one @@ -44,10 +60,29 @@ export function useTranscripts(apiBase, dataset, viewport, imageSize, enabled = try { let url = `${apiBase}/spatial/${dataset}/transcripts?fraction=${fraction}`; - // Send selected gene filter so backend samples within those species only. + // Send the selected gene filter so the backend samples within those + // species only — stated as an allowlist or as its complement, whichever + // is shorter. + // + // This is a GET, so the list travels in the URL and long ones are fatal: + // measured against this stack, 479 of 480 genes is a 7,780-byte URL, only + // ~220 bytes under nginx's 8 KB request-line limit, and 600 genes returns + // 414 outright. Deselecting a handful of genes from a large panel is an + // ordinary thing to do and produces exactly that shape, so the naive + // allowlist breaks on any panel much past 500 genes — a Xenium Prime 5K + // run would fail on the first click. + // + // The complement is small precisely when the allowlist is not, so sending + // the smaller of the two bounds the URL at roughly half the panel. if (selectedGenes !== null && selectedGenes.size > 0) { - for (const g of selectedGenes) { - url += `&genes=${encodeURIComponent(g)}`; + const total = Array.isArray(allGenes) ? allGenes.length : 0; + const excluded = total > 0 + ? allGenes.filter((g) => !selectedGenes.has(g)) + : []; + if (total > 0 && excluded.length < selectedGenes.size) { + for (const g of excluded) url += `&exclude_genes=${encodeURIComponent(g)}`; + } else { + for (const g of selectedGenes) url += `&genes=${encodeURIComponent(g)}`; } } @@ -76,7 +111,10 @@ export function useTranscripts(apiBase, dataset, viewport, imageSize, enabled = }, 200); return () => clearTimeout(timerRef.current); - }, [apiBase, dataset, viewport?.xmin, viewport?.ymin, viewport?.xmax, viewport?.ymax, enabled, fraction, genesKey]); // eslint-disable-line + // allGenes.length is a dep because it decides the URL *form*: before the panel + // loads there is no complement to compute, so an early fetch would fall back to + // the long allowlist and could 414. Re-running once it arrives fixes that. + }, [apiBase, dataset, viewport?.xmin, viewport?.ymin, viewport?.xmax, viewport?.ymax, enabled, fraction, genesKey, allGenes?.length]); // eslint-disable-line // Abort in-flight request on unmount useEffect(() => { From b60105909823accbf06d25e2171c1975f6db2bc0 Mon Sep 17 00:00:00 2001 From: Micha Sam Brickman Raredon <42100128+msraredon@users.noreply.github.com> Date: Thu, 13 Aug 2026 10:52:24 -0400 Subject: [PATCH 12/13] chore: v0.8.4 Co-Authored-By: Claude Opus 5 --- backend/app/main.py | 2 +- frontend/package.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/backend/app/main.py b/backend/app/main.py index 124a35a..ee1c810 100644 --- a/backend/app/main.py +++ b/backend/app/main.py @@ -3,7 +3,7 @@ from app.routers import tiles, spatial, edges, layers -APP_VERSION = "0.8.3" +APP_VERSION = "0.8.4" app = FastAPI(title="TissuePlex API", version=APP_VERSION) diff --git a/frontend/package.json b/frontend/package.json index 76f4836..d9ed916 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -1,6 +1,6 @@ { "name": "tissueplex", - "version": "0.8.3", + "version": "0.8.4", "private": true, "scripts": { "dev": "vite", From e835f7fb813b19f3e432859009b96e72c092a6db Mon Sep 17 00:00:00 2001 From: Micha Sam Brickman Raredon <42100128+msraredon@users.noreply.github.com> Date: Thu, 13 Aug 2026 10:55:47 -0400 Subject: [PATCH 13/13] =?UTF-8?q?fix:=20usePanels.js=20was=20a=20binary=20?= =?UTF-8?q?file=20=E2=80=94=20literal=20NUL=20in=20the=20source?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit useUnionList joins dataset names on a NUL to build its effect key, which is sound (a NUL cannot appear in a directory name, so two datasets can never collide into one key), but the byte was written literally into the source rather than as an escape. Git classifies any file containing a NUL as binary, so the file showed as "Bin 0 -> 3418 bytes" in every diff: no line-level review, no blame, no meaningful conflict resolution. The escape form is the identical value at runtime and keeps the file text. Co-Authored-By: Claude Opus 5 --- frontend/src/hooks/usePanels.js | Bin 3418 -> 3725 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/frontend/src/hooks/usePanels.js b/frontend/src/hooks/usePanels.js index d1ed589fa6eb5e653222020fcdbe95b21e6734d9..42065697605a937feed7b61688aafb553deb0c22 100644 GIT binary patch delta 325 zcmXAkJxT;Y5QV|OV2&Ut?*RK_>=8sZ6BCo3?i#1qp6;cpW`~LP1V-W&1T(=icnMEq zty5XSd*6HS_v-cf>$5p+x45rdD4`aF0@4@`{_P$gR+xH@Jz^-O8fuKAfL(>eLeZ+s zC}E%#w8Ey^08UH6f|BXkhC)XY#5%Md;a+oLPf&MZPxA(+Ct)*ksN#9N^@9YL$O5z8 zApx0hhn7-W>S73cQoOJs=Ag>m>|v}6N$V()&~c`7miGxVudrSw n>UcN^sk%RL*Qv9UrxsdgUyvbmWSTZ_AI;6>QuF(#dB6ArR|;?z delta 41 wcmeB`y(P6_C6`QceqM2jLUwAUf~`VIVo73gYDuwPR(@ulh7!Z(AKX)!078Eb`2YX_

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