In-Silico Knockout Experiments from Single-Cell Gene Regulatory Networks

A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells.


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1.0.1 by Daniel Osorio, 3 months ago

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Authors: Daniel Osorio [aut, cre] , Yan Zhong [aut, ctb] , Guanxun Li [aut, ctb] , Qian Xu [aut, ctb] , Andrew Hillhouse [aut, ctb] , Jingshu Chen [aut, ctb] , Laurie Davidson [aut, ctb] , Yanan Tian [aut, ctb] , Robert Chapkin [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, scTenifoldNet

Suggests testthat

See at CRAN