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.

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 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>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: scTenifoldNet citation info
Materials: README
In views: Omics
CRAN checks: scTenifoldNet results


Reference manual: scTenifoldNet.pdf


Package source: scTenifoldNet_1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): scTenifoldNet_1.3.tgz, r-oldrel (arm64): scTenifoldNet_1.3.tgz, r-release (x86_64): scTenifoldNet_1.3.tgz, r-oldrel (x86_64): scTenifoldNet_1.3.tgz
Old sources: scTenifoldNet archive

Reverse dependencies:

Reverse imports: scTenifoldKnk


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