End-to-end analysis of the PBMC 3k single-cell RNA-seq dataset using Python and Scanpy, including preprocessing, UMAP visualization, Leiden clustering, cell type annotation, and marker gene identification.
Comparison of Seurat normalization workflows and manual vs. SeuratData cluster labeling on the PBMC3k dataset.
scGPT fine-tuning on PBMC3k · 7-method benchmark · Nature Methods 2024 framework
An end-to-end scRNA-seq analysis pipeline processing raw PBMC data through QC, dimensionality reduction, and clustering, deployed as an interactive, production-ready R Shiny dashboard.
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