单细胞转录组相关分析工具安装流程
mamba create -n scanpy conda-forge:scanpy=1.12.1 scikit-image -y
mamba activate scanpy
# 为了联合使用R
mamba install -c bioconda anndata2ri -y
# 为了聚类
mamba install conda-forge:python-igraph -y
# 为了高变基因
mamba install conda-forge::scikit-misc -y
# 为了转成 R 格式 easySCF
#module purge;module load compiler/gcc/9.3.0
#mamba install scipy -y
#pip install --upgrade --force-reinstall git+https://github.com/Efdix/easySCF.git#subdirectory=py
# 为了统计
mamba install conda-forge::statannotations -y
# 为了命令行版把矩阵传入R
R
install.packages('Matrix')
mamba install main::matplotlib-venn -y
mamba install conda-forge::moscot -y
# 为了在终端中执行notebook
mamba install conda-forge::nbconvert -y
mamba install ipykernel -y
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate scanpy
python -m ipykernel install --name scanpy --display-name scanpy
# （容器外命令行用：python -m ipykernel install --sys-prefix --name scanpy --display-name scanpy）
conda deactivate
mamba create -n scvi-tools conda-forge:scvi-tools=1.4.2 conda-forge:scanpy=1.12.1 -y
mamba activate scvi-tools
mamba install conda-forge::scikit-misc -y
mamba install -c conda-forge python-igraph -y
# 为了在终端中执行notebook
mamba install conda-forge::nbconvert -y
mamba install ipykernel -y
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate scvi-tools
python -m ipykernel install --name scvi-tools --display-name scvi-tools
# （容器外命令行用：python -m ipykernel install --sys-prefix --name scvi-tools --display-name scvi-tools）
conda deactivate
mamba create -n pertpy conda-forge:pertpy=1.0.6 conda-forge:scanpy=1.12.1 python=3.13 -y
conda activate pertpy
# 为了进行差异分析
pip install 'pertpy[de]'
pip install pydeseq2
pip install decoupler
# 为了用edger
mamba install conda-forge::rpy2 -y
# 为了使用tcoda
mamba install schrodinger::pyqt6 -y
pip install 'pertpy[tcoda]'
# 画图
mamba install galaxy001::matplotlib_venn -y
# 细胞比例工具 scanpro
module purge;module load compiler/gcc/9.3.0 #gcc版本不对导致pip连一些基础的东西都装不上
pip install scanpro
mamba install ipykernel -y
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate pertpy
python -m ipykernel install --name pertpy --display-name pertpy
conda deactivate
mamba create -n cpdb scanpy=1.12.1 -y
mamba activate cpdb
module purge;module load compiler/gcc/9.3.0
pip install cellphonedb
# 下载数据库，注意要解压后其中的cellphonedb.zip才是真正的数据库，不能直接用cellphonedb-data-5.0.0.zip
# https://github.com/ventolab/cellphonedb-data # v5.0.0
# 存储路径：/work/home/acfrxahp1e/software/cpdb/db/v5/cellphonedb.zip
mamba install ipykernel -y
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate cpdb
python -m ipykernel install --name cpdb --display-name cpdb
conda deactivate
mamba create -n pyscenic bioconda::pyscenic -y
mamba activate pyscenic
mamba install "setuptools<70.0.0" -y
mamba install scanpy -y
mamba install ipykernel -y
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate pyscenic
python -m ipykernel install --name pyscenic --display-name pyscenic
conda deactivate
mamba create -n cellrank scanpy=1.12.1 pip uv scikit-learn h5py pandas=2.3.3 contourpy=1.3.3 matplotlib==3.10.8 -y
mamba activate cellrank
uv pip install cellrank
# 为了RNA速率
mamba install conda-forge::scvelo -y
mamba install conda-forge::loompy -y
# 为了Estimator
mamba install -c conda-forge petsc4py slepc4py -y
# 为了高变基因
mamba install scikit-misc -y
mamba install ipykernel -y
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate cellrank
python -m ipykernel install --name cellrank --display-name cellrank
conda deactivate
# git clone https://github.com/digitalcytometry/cytotrace2
cd /work/home/acfrxahp1e/zhaohz/software/cytotrace2/cytotrace2_python
mamba env create -f environment_py.yml -y
conda activate cytotrace2-py
pip install .
mamba create -n pseudotime -c conda-forge -c bioconda scanpy=1.12.1 palantir=1.4.4 -y
conda activate pseudotime
# paga补充
pip install fa2-modified
# slingshot
module purge;module load compiler/gcc/9.3.0
pip install pyslingshot
mamba install conda-forge::r-seurat=5.4.0 bioconda::r-monocle3 bioconda::bioconductor-monocle -y
mamba install ipykernel -y
mamba install r-irkernel -ymamba install r-irkernel -y
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate pseudotime
python -m ipykernel install --name pseudotime --display-name pseudotime
conda deactivate
conda activate pseudotime
R -e "IRkernel::installspec(name = 'pseudotime', displayname = 'pseudotime')"
conda deactivate
# Clone git repo.
git clone https://github.com/aertslab/create_cisTarget_databases
cd create_cisTarget_databases
# Display to which value ${create_cistarget_databases_dir} variable should be set.
echo "create_cistarget_databases_dir='""${PWD}""'"
# Create conda environment.
mamba create -n create_cistarget_databases \
'python=3.10' \
'numpy=1.21' \
'pandas>=1.4.1' \
'pyarrow>=7.0.0' \
'numba>=0.55.1' \
'python-flatbuffers'
# Install Cluster-Buster
# Activate conda environment.
conda activate create_cistarget_databases
cd "${CONDA_PREFIX}/bin"
# Download precompiled Cluster-Buster binary.
wget https://resources.aertslab.org/cistarget/programs/cbust
# Make downloaded binary executable.
chmod a+x cbust
# Clone Cluster-Buster repo.
git clone https://github.com/weng-lab/cluster-buster
cd cluster-buster
# Compile Cluster-Buster.
make cbust
# Activate conda environment.
conda activate create_cistarget_databases
# Copy CLuster-Buster binary of your choice in conda environment.
cp -a cbust "${CONDA_PREFIX}/bin/cbust"
# Install UCSC tools
# Activate conda environment.
conda activate create_cistarget_databases
cd "${CONDA_PREFIX}/bin"
# Download liftOver.
wget http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/liftOver
# Download bigWigAverageOverBed.
wget http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bigWigAverageOverBed
# Make downloaded binaries executable.
chmod a+x liftOver bigWigAverageOverBed
mamba create -n Seurat5 conda-forge::r-seurat=5.4.0 -y
mamba activate Seurat5
mamba install bioconda::bioconductor-scran -y
mamba install r-irkernel -ymamba install r-irkernel -y
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate Seurat5
R -e "IRkernel::installspec(name = 'Seurat5', displayname = 'Seurat5')"
conda deactivate
mamba create -n Clustree conda-forge::r-seurat=5.4.0 -y
mamba activate Clustree
mamba install r-irkernel -y
mamba install -c conda-forge r-tweenr r-systemfonts r-ggforce r-graphlayouts r-backports r-ggraph r-checkmate r-viridis r-tidygraph -y
R
install.packages("clustree") #下载时选18合肥线比较顺利
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate Clustree
R -e "IRkernel::installspec(name = 'Clustree', displayname = 'Clustree')"
conda deactivate
mamba create -n Enrich conda-forge::r-base=4.5.3 -y
mamba activate Enrich
mamba install conda-forge::r-tidyverse -y
mamba install bioconda::bioconductor-clusterprofiler -y
mamba install r-irkernel -y
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate Enrich
R -e "IRkernel::installspec(name = 'Enrich', displayname = 'Enrich')"
conda deactivate
mamba create -n ClusterGVis seurat r-devtools bioconductor-complexheatmap bioconductor-tcseq -y
conda activate ClusterGVis
devtools::install_github("junjunlab/ClusterGVis")
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate ClusterGVis
R -e "IRkernel::installspec(name = 'ClusterGVis', displayname = 'ClusterGVis')"
conda deactivate
mamba create -n bioconda::bioconductor-complexheatmap r::r-factoextra r::r-dendextend r::r-rcolorbrewer r::r-tidyverse bioconda::bioconductor-edger bioconda::bioconductor-deseq2 bioconda::bioconductor-limma conda-forge::r-magick -y
conda activate bulk
# 手动下载GenomeInfoDbData包，然后让A老师在bulk环境中本地安装
# https://mghp.osn.xsede.org/bir190004-bucket01/archive.bioconductor.org/packages/3.18/data/annotation/src/contrib/GenomeInfoDbData_1.2.11.tar.gz
mamba install r::r-venndiagram
mamba install r::r-ggvenndiagram
mamba install r::r-irkernel jupyter -y
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate bulk
R -e "IRkernel::installspec(name = 'bulk', displayname = 'bulk')"
conda deactivate
mamba create -n cellchat bioconda::bioconductor-biocgenerics dnachun::r-cellchat=2.1.2 -y
conda activate cellchat
mamba install -c bioconda -c dnachun r-nmf=0.28 -y
mamba install r-irkernel -y
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate cellchat
R -e "IRkernel::installspec(name = 'cellchat', displayname = 'cellchat')"
conda deactivate
# 备注，最新版是让A老师直接装的，用mamba装不了最新版
mamba create -n anndatar bioconda::bioconductor-anndatar -y
conda activate anndatar
R
install.packages("BiocManager")
mamba install bioconda::bioconductor-rhdf5 bioconda::bioconductor-singlecellexperiment conda-forge::r-seurat conda-forge::r-seuratobject -y
mamba install r-irkernel -y
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate anndatar
R -e "IRkernel::installspec(name = 'anndatar', displayname = 'anndatar')"
conda deactivate
mamba create -n deconv r::r-devtools -y
conda activate deconv
mamba install -c conda-forge -c bioconda r-snowfall r-nmf r-gplots bioconductor-scran bioconductor-biocparallel r::r-irkernel jupyter r-dplyr -y
R
library("devtools");
install_github("Danko-Lab/BayesPrism/BayesPrism")
install.packages("dplyr")
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate deconv
R -e "IRkernel::installspec(name = 'deconv', displayname = 'deconv')"
conda deactivate
mamba create -n orthofinder3 bioconda::orthofinder -y
wget https://ftp-trace.ncbi.nlm.nih.gov/sra/sdk/3.2.0/sratoolkit.3.2.0-centos_linux64.tar.gz #下载3.2.0版，曙光不支持最新版
tar -zxvf sratoolkit.3.2.0-centos_linux64.tar.gz
rm sratoolkit.3.2.0-centos_linux64.tar.gz
export PATH=$PWD/sratoolkit.3.2.0-centos_linux64/bin:$PATH #注意PWD的路径是否正确
mamba create -n repeatmasker bioconda::repeatmodeler=2.0.7 bioconda::repeatmasker -y # 不一起装容易导致python版本过高
mamba activate repeatmasker
# 安装搜索repeats的软件，有多个选择：Cross_Match、RMBlast and WUBlast/ABBlast等，这里用RMBlast和hmmer
mamba install bioconda::rmblast=2.14.1 -y
mamba install bioconda::hmmer -y
# https://www.repeatmasker.org/RepeatMasker 下载repeatmasker源码
tar -xvzf RepeatMasker-4.2.3.tar.gz
cd /work/home/acfrxahp1e/zhaohz/software/RepeatMasker
./configure
# 回车后填入TRF路径，正常应该在第一步mamba时已经被安装了，大概率是
/work/home/acfrxahp1e/miniforge3/envs/repeatmasker/bin/trf
# 选RMBlast并填入路径/work/home/acfrxahp1e/miniforge3/envs/repeatmasker/bin，注意末尾不要带上软件名
# 选hmmer并填入/work/home/acfrxahp1e/miniforge3/envs/repeatmasker/bin
# 不要把hmmer设为默认
mamba create -n star bioconda::star -y
mamba activate star
# velocyto
mamba create -n velocyto numpy scipy cython numba matplotlib scikit-learn h5py click bioconda::velocyto.py -y
mamba create -n eggnog-mapper -c bioconda eggnog-mapper -y
mamba activate eggnog-mapper
# R
mamba install r::r-tidyverse -y
mamba install bioconda::bioconductor-annotationforge -y
mamba install bioconda::bioconductor-s4vectors=0.48.0 -y
mamba install bioconda::bioconductor-go.db -y
mamba install r-irkernel -y
/work/home/acfrxahp1e/miniforge3/bin/conda init
conda activate eggnog-mapper
R -e "IRkernel::installspec(name = 'eggnog-mapper', displayname = 'eggnog-mapper')"
# 在 PowerShell 中运行以下命令，查看当前用户的配置文件路径：
$PROFILE
# 使用文本编辑器（如 VS Code 或 Notepad）打开配置文件：
code $PROFILE # D:\System\Documents\WindowsPowerShell\Microsoft.PowerShell_profile.ps1
# 在配置文件中添加以下内容：
mamba.exe shell hook -s powershell | Out-String | Invoke-Expression
# 保存并关闭配置文件
# 重新启动 PowerShell
jupyter kernelspec list # 查看添加了哪些内核
jupyter kernelspec uninstall xxx -y # 删除xxx内核（注意替换xxx）
mamba install pandas -y
mamba install conda-forge::pandoc -y
mamba install bioconda::pysradb -y
pip install biopython
# 1. 创建新环境 (Python 3.9 或 3.10 均可)
mamba create -n chatcellanno python=3.9 -y
# 2. 激活环境
conda activate chatcellanno
# 3. 安装必要的轻量级依赖
mamba install pandas pyperclip pyinstaller openpyxl -y
#pip install windnd （改为pyside6后就不需要这个了）
# (安装 openpyxl 是为了支持未来可能读取 excel，目前 pandas 需要它)
# 4. 确保 tkinter 已安装 (通常 python 自带，如果没有报错则跳过)
python -c "import tkinter; print('Tkinter OK')"
# 5. 界面化
mamba install main::pyside6 -y
pip install gseapy # 不能用mamba，bioconda上的有问题
mamba create -n word-palace -y
mamba activate word-palace
mamba install pip -y
pip install customtkinter keyboard pyperclip requests pystray pillow
pip install -i https://mirrors.tencent.com/pypi/simple/ --upgrade tencentcloud-sdk-python