scTenifoldNet - 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.

Last updated 2 months ago

differential-regulation-analysisgene-regulatory-networksmanifold-alignmentsingle-celltensor-decomposition

5.63 score 22 stars 1 dependents 65 scripts 321 downloads

scTenifoldKnk - In-Silico Knockout Experiments from Single-Cell Gene Regulatory Networks

A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells.

Last updated 2 months ago

functional-genomicsgene-functiongene-knockoutgene-regulatory-networkvirtual-knockout-experiments

4.85 score 43 stars 11 scripts 158 downloads