The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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 <dcosorioh at utexas.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 |
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: scTenifoldNet_1.3.zip, r-release: scTenifoldNet_1.3.zip, r-oldrel: scTenifoldNet_1.3.zip |
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 imports: | scTenifoldKnk |
Please use the canonical form https://CRAN.R-project.org/package=scTenifoldNet to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.