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 for more details.


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1.3 by Daniel Osorio, 3 months ago

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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, RhpcBLASctl

Suggests testthat

Imported by scTenifoldKnk.

See at CRAN