基于 python 的单细胞转录因子分析

pyscenic

文章目录

基于 python 的单细胞转录因子分析
前言
Main

前言

流程极为简单，几乎没有任何难度

Main

Install pyscenic

！Attention, python version >=3.7

pip install pyscenic

Download reference datas

wget -c https://github.com/aertslab/pySCENIC/archive/refs/heads/master.zip
x master.zip
cd master
mv resources/* ../../
wget -c https://resources.aertslab.org/cistarget/motif2tf/motifs-v9-nr.hgnc-m0.001-o0.0.tblwget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/tc_v1/gene_based/encode_20190621__ChIP_seq_transcription_factor.hg19-tss-centered-5kb.max.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/tc_v1/gene_based/encode_20190621__ChIP_seq_transcription_factor.hg19-500bp-upstream.max.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/tc_v1/gene_based/encode_20190621__ChIP_seq_transcription_factor.hg19-tss-centered-10kb.max.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/tc_v1/gene_based/encode_20190621__ChIP_seq_transcription_factor.hg38__refseq-r80__10kb_up_and_down_tss.max.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/tc_v1/gene_based/encode_20190621__ChIP_seq_transcription_factor.hg38__refseq-r80__500bp_up_and_100bp_down_tss.max.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc8nr/gene_based/hg19-500bp-upstream-7species.mc8nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc9nr/gene_based/hg19-500bp-upstream-7species.mc9nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc8nr/gene_based/hg19-500bp-upstream-10species.mc8nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc9nr/gene_based/hg19-500bp-upstream-10species.mc9nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc9nr/gene_based/hg38__refseq-r80__500bp_up_and_100bp_down_tss.mc9nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc8nr/gene_based/hg19-tss-centered-10kb-7species.mc8nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc9nr/gene_based/hg19-tss-centered-10kb-7species.mc9nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc9nr/gene_based/hg38__refseq-r80__10kb_up_and_down_tss.mc9nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc8nr/gene_based/hg19-tss-centered-10kb-10species.mc8nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc9nr/gene_based/hg19-tss-centered-10kb-10species.mc9nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc8nr/gene_based/hg19-tss-centered-5kb-7species.mc8nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc9nr/gene_based/hg19-tss-centered-5kb-7species.mc9nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc9nr/gene_based/hg19-tss-centered-5kb-7species.mc9nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc8nr/gene_based/hg19-tss-centered-5kb-10species.mc8nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc9nr/gene_based/hg19-tss-centered-5kb-10species.mc9nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc8nr/region_based/hg19-regions-9species.all_regions.mc8nr.feather
wget -c https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc9nr/region_based/hg19-regions-9species.all_regions.mc9nr.feather

The pipline of pyscenic only 3 steps

Step.1


pyscenic grn \--num_workers 6 \-o /data/expr_mat.adjacencies.tsv \ # input Count data# csv (rows=cells x columns=genes) or loom (rows=genes x columns=cells)./data/expr_mat.tsv \/data/allTFs_hg38.txt

Step.2

pyscenic ctx \/data/expr_mat.adjacencies.tsv \ # First Step out put file/data/hg19-tss-centered-5kb-7species.mc9nr.feather \/data/hg19-tss-centered-10kb-7species.mc9nr.feather \--annotations_fname /data/motifs-v9-nr.hgnc-m0.001-o0.0.tbl \--expression_mtx_fname /data/expr_mat.tsv \ # the same to the first input data--mode "dask_multiprocessing" \--output /data/regulons.csv \--num_workers 6

Step.3

pyscenic aucell \/data/expr_mat.tsv \/data/regulons.csv \-o /data/auc_mtx.csv \--num_workers 6

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基于 python 的单细胞转录因子分析

基于 python 的单细胞转录因子分析

文章目录

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Main

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