cailab-tamu/PCrTdMa: Construct and Compare scGRN from Single-Cell Transcriptomic Data

A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See <doi:10.1016/j.patter.2020.100139> for more details.

Getting started

Package details

Maintainer
LicenseGPL (>=2)
Version1.2.4
URL https://github.com/cailab-tamu/scTenifoldNet
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("remotes")
remotes::install_github("cailab-tamu/PCrTdMa")
cailab-tamu/PCrTdMa documentation built on April 19, 2021, 12:11 a.m.