###---------------------1. Download demo singlecell sequencing data---------------###
wget http://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_10k_v3/pbmc_10k_v3_filtered_feature_bc_matrix.tar.gz
tar xvf pbmc_10k_v3_filtered_feature_bc_matrix.tar.gz
###----------------------2. Creat seurat object and perform quality control-----------------------------###
library(Seurat)
subset_cells <- Read10X(data.dir = "./filtered_feature_bc_matrix")
subset_cells <- CreateSeuratObject(subset_cells)
mito.features <- grep(pattern = "^MT-", x = rownames(x = subset_cells), value = TRUE)
percent.mito <- Matrix::colSums(x = GetAssayData(object = subset_cells, slot = 'counts')[mito.features, ])/Matrix::colSums(x = GetAssayData(object = subset_cells, slot = 'counts'))
subset_cells[["percent.mito"]] <- percent.mito
subset_cells <- subset(x = subset_cells, subset = nFeature_RNA >= 500 & nFeature_RNA < 7000 & percent.mito <= 0.20 & nCount_RNA >= 1000 & nCount_RNA <= 70000)
# TenXdat@meta.data %>% row.names %>% write.table(file = paste0(project, ".RData"), col.names = F ,row.names = F, sep = "\t")
subset_cells <- subset_cells[, sample(1:10905, 500)] ##sampling 500 cells for demo
sce_count <- GetAssayData(subset_cells[["RNA"]], slot = "counts")
write.csv(sce_count, file="sce_count.csv")
###----------------------3.Download new database for SCINEC-----------------###
###genome ranking database
wget -c --no-check-certificate https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc_v10_clust/gene_based/hg38_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather
wget -c --no-check-certificate https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc_v10_clust/gene_based/hg38_500bp_up_100bp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather
###motif–TF annotation
wget -c --no-check-certificate https://resources.aertslab.org/cistarget/motif2tf/motifs-v10nr_clust-nr.hgnc-m0.001-o0.0.tbl
###TF list
wget -c --no-check-certificate https://resources.aertslab.org/cistarget/tf_lists/allTFs_hg38.txt
###----------------------4.Create conda environment-----------------###
conda create -y -n pyscenic python=3.10
conda activate pyscenic
### very important step: re-install numpy 1.23.4
pip install numpy==1.23.4
###----------------------5.Run pySCENIC-----------------###
## expression matrix
f_ex_matrix_csv=/data4/heshuai/tmp/pySCINEC/sce_count.csv
## referrence databases
f_db_names=$(ls /data/home/heshuai/reference_data/*feather | tr "\n" " ")
f_motif_path=/data/home/heshuai/reference_data/motifs-v10nr_clust-nr.hgnc-m0.001-o0.0.tbl
f_tfs=/data/home/heshuai/reference_data/allTFs_hg38.txt
##--------------------Step 1: GENIE3
pyscenic grn \
--output adjusted.tsv \
--method grnboost2 \
--transpose \
${f_ex_matrix_csv} \
${f_tfs} \
--num_workers 48
##--------------------Step 2: cistarget
pyscenic ctx adjusted.tsv \
${f_db_names} \
--annotations_fname ${f_motif_path} \
--expression_mtx_fname ${f_ex_matrix_csv} \
--transpose \
--output sce_regulon.gmt \
--mask_dropouts \
--mode "dask_multiprocessing" \
--num_workers 40
###--------------------Step 3: aucell
pyscenic aucell \
${f_ex_matrix_csv} \
sce_regulon.gmt \
--transpose \
--output sce_regulon_AUC.csv \
--num_workers 48
参考：https://github.com/HeShuai/Daily_tools_HeShuai/blob/main/2.%20Analyisis_TFs_of_singlecell_data_using_pySCENIC
