PySCNet: A tool for reconstructing and analyzing gene regulatory network from single-cell RNA-Seq data

There are four modules:

1. Pro-precessing: initialize a gnetData object consisting of Expression Matrix, Cell Attributes, Gene Attributes and Network Attributes;
2. BuildNet: reconstruct GRNs by various methods implemented in docker;
3. NetEnrich: network analysis including consensus network detection, gene module identification and trigger path prediction as well as network fusion;
4. Visulization: network illustration.

🎉 🎊 Create your own GRNs

Shinyapp is available now for creating your own GRNs. Once the cells are grouped into several clusters and linkage tables are generated for each/all clusters, you can export the results as pickle object and uplaod onto Shinyapp. Cell attributes, Gene attributes and Network attributes are illustrated here. As shown belows, you can set your own thresholds to build each/all cluster-specific GRNs.

Installation

1. clone from github: git clone https://github.com/MingBit/PySCNet
2. create a new folder and set up: mkdir dist | python setup.py sdist
3. install pyscnet: pip install dist/pyscnet-0.0.2.tar.gz

Tutorial

PBMC data preprocessed and analyzed by scanpy as explained in this tutorial.

TO-DO

1. Add an Auto-ML based pipeline to Pre-Processing module;
2. Collect more GRN methods to BuildNet module;
3. Update other network-based algorithms to NetEnrich module;
4. Build R-Shiny app for visualization
5. Test with integrated sc RNA-seq data.

Cite

• 😸 This work has been presented at ISMB/ECCB 2019;
• 🐾 Go to my poster;
• 📃 Reference: Wu M, Kanev K, Roelli P and Zehn D. PySCNet: A tool for reconstructing and analyzing gene regulatory network from single-cell RNA-Seq data [version 1; not peer reviewed]. F1000Research 2019, 8(ISCB Comm J):1359 (poster) (doi: 10.7490/f1000research.1117280.1)