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:	1.2.2
Imports:	pbapply, RSpectra, Matrix, methods, stats, utils, MASS
Suggests:	testthat (≥ 2.1.0)
Published:	2020-05-13
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 <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

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