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.

Version: 1.2.0
Imports: reticulate, pbapply, RSpectra, Matrix, methods, stats, utils, MASS
Suggests: testthat (≥ 2.1.0)
Published: 2020-03-10
Author: Daniel Osorio ORCID iD [aut, cre], Yan Zhong [aut, ctb], Guanxun Li [aut, ctb], Jianhua Huang [aut, ctb], James Cai ORCID iD [aut, ctb, ths]
Maintainer: Daniel Osorio <dcosorioh at tamu.edu>
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
CRAN checks: scTenifoldNet results

Downloads:

Reference manual: scTenifoldNet.pdf
Package source: scTenifoldNet_1.2.0.tar.gz
Windows binaries: r-devel: scTenifoldNet_1.2.0.zip, r-devel-gcc8: scTenifoldNet_1.2.0.zip, r-release: scTenifoldNet_1.2.0.zip, r-oldrel: scTenifoldNet_1.2.0.zip
OS X binaries: r-release: scTenifoldNet_1.2.0.tgz, r-oldrel: scTenifoldNet_1.2.0.tgz
Old sources: scTenifoldNet archive

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