Package: scTenifoldNet 1.3

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.

Authors:Daniel Osorio [aut, cre], Yan Zhong [aut, ctb], Guanxun Li [aut, ctb], Jianhua Huang [aut, ctb], James Cai [aut, ctb, ths]