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scTenifoldNet

Constructing and comparing gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNAseq) data present formidable computational challenges. Many existing solutions lack effectiveness or efficiency, due to technical and analytical issues of scRNAseq such as random dropout, uncharacterized cell states, and heterogeneous samples.

scTenifoldNet is an unsupervised machine learning workflow that combines principal component regression, low-rank tensor approximation, and manifold alignment. It constructs and compares transcriptome-wide single-cell GRNs (scGRNs) from different samples to identify gene expression signatures shifting with cellular activity changes such as pathophysiological processes and responses to environmental perturbations.