Published 2023 | Version 1.0.1
Dataset Open

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

Creators

  • 1. ROR icon Columbia University
  • 2. ROR icon Centre for Genomic Regulation
  • 3. ROR icon Chan Zuckerberg Initiative (United States)

Description

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

Files

Files (1.4 GB)

Name Size Download all
md5:a64b3c528fff7e7f880421799efdb458
105.7 MB Download
md5:cb7e167c4dfd49a1a631b95b0934105e
205.6 MB Download
md5:1fdff14a9d23cdad497cd10cc8dc30f5
91.5 MB Download
md5:863b5eeaa85fd503a4a5eccb2503ef45
177.4 MB Download
md5:3d7b41809d784aa54fd306521501d313
107.3 MB Download
md5:0100b34ebfc0629c3f798dfc4f14abf4
208.1 MB Download
md5:8178ce54afaf81b4306b7c6dc423486e
107.8 MB Download
md5:259132d07c99ed70d699f4ce9b1001e5
204.6 MB Download
md5:1688307c0365b8808655403e668b129d
150.5 MB Download
md5:a2116e80691bccfea63d60993075086d
16.5 MB Download
md5:23620b1c292c2ec9ae9c2554c250a9ff
30.6 kB Download

Additional details

References

  • Alvarez, M., Shen, Y., Giorgi, F. et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat Genet 48, 838–847 (2016). https://doi.org/10.1038/ng.3593
  • Griffin, A. T., Vlahos, L. J., Chiuzan, C. & Califano, A. NaRnEA: An Information Theoretic Framework for Gene Set Analysis. Entropy 25, 542 (2023). https://doi.org/10.3390/e25030542
  • Peng, J., Sun, BF., Chen, CY. et al. Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma. Cell Res 29, 725–738 (2019). https://doi.org/10.1038/s41422-019-0195-y##
  • Basso, K., Margolin, A., Stolovitzky, G. et al. Reverse engineering of regulatory networks in human B cells. Nat Genet 37, 382–390 (2005). https://doi.org/10.1038/ng1532