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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.
Getting started
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Package details |
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| Author | Daniel Osorio [aut, cre] (<https://orcid.org/0000-0003-4424-8422>), Yan Zhong [aut, ctb], Guanxun Li [aut, ctb], Jianhua Huang [aut, ctb], James Cai [aut, ctb, ths] (<https://orcid.org/0000-0002-8081-6725>) |
| Maintainer | Daniel Osorio <dcosorioh@utexas.edu> |
| License | GPL (>= 2) |
| Version | 1.3 |
| URL | https://github.com/cailab-tamu/scTenifoldNet |
| Package repository | View on CRAN |
| Installation |
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