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.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.2.3 by Daniel Osorio, 2 months ago

Report a bug at

Browse source code at

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

Documentation:   PDF Manual  

GPL (>= 2) license

Imports pbapply, RSpectra, Matrix, methods, stats, utils, MASS

Suggests testthat

See at CRAN