Welcome to pyscnet’s documentation!

PySCNet: Introduction

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

Modules

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.

_images/Overview.png

Features

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.

_images/ShinyApp.gif

Cite

Installation

Requirements

As pyscnet integrates docker for gene regulatory construction, it is necessary to install docker before the installation. Check here for docker installation.

PyPI

install pyscnet via PyPI

pip install pyscnet

Github

install pyscnet via github

git clone https://github.com/MingBit/PySCNet
mkdir dist | python setup.py sdist
pip install dist/pyscnet-0.0.2.tar.gz

Indices and tables