scTenifoldNet: Construct and Compare scGRN from Single-Cell Transcriptomic Data (original) (raw)
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.
Version: | 1.3 |
---|---|
Imports: | pbapply, RSpectra, Matrix, methods, stats, utils, MASS, RhpcBLASctl |
Suggests: | testthat (≥ 2.1.0) |
Published: | 2021-10-29 |
DOI: | 10.32614/CRAN.package.scTenifoldNet |
Author: | Daniel Osorio [aut, cre], Yan Zhong [aut, ctb], Guanxun Li [aut, ctb], Jianhua Huang [aut, ctb], James Cai [aut, ctb, ths] |
Maintainer: | Daniel Osorio |
BugReports: | https://github.com/cailab-tamu/scTenifoldNet/issues |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
URL: | https://github.com/cailab-tamu/scTenifoldNet |
NeedsCompilation: | no |
Citation: | scTenifoldNet citation info |
Materials: | README |
In views: | Omics |
CRAN checks: | scTenifoldNet results |
Documentation:
Downloads:
Reverse dependencies:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=scTenifoldNetto link to this page.