PyVIPER: A fast and scalable Python package for rank-based enrichment analysis of single-cell RNASeq data

Data Repository for VIPER analysis in Python. These data are used in Tutorials of the PyVIPER package. All files were generated by post-processing publicly available data from pancreatic ductal adenocarcinoma (PDAC) patients by Peng et al., 2019. The files included are: B-cell-net.tsv: ARACNe3-inferred gene regulatory network for B cells in PDAC ductal-2-net.tsv: ARACNe3-inferred gene regulatory network for malignant ductal cell type 2 in PDAC fibroblast-net.tsv: ARACNe3-inferred gene regulatory network for fibroblasts in PDAC stellate-net.tsv: ARACNe3-inferred gene regulatory network for stellate cells in PDAC Tutorial_1_gExpr_fibroblast_5802.tsv.gz: gene expression signature calculated for 5802 cells (fibroblasts) used in Tutorial 1 Tutorial_2_counts_mixed_4632.tsv.gz: UMI matrix for 4632 cells from different cellular populations used in Tutorial 2 Tutorial_2_metadata_mixed_4632.tsv.gz: metadata for 4632 cells from different cellular populations used in Tutorial 2 Files in .pkl format are the ARACNe3-inferred gene regulatory networks for the specific cell population in PDAC in .pkl format