Construct and Compare scGRN from Single-Cell Transcriptomic Data
Canonical Polyadic Decomposition
Performs counts per million (CPM) data normalization
Evaluates gene differential regulation based on manifold alignment dis...
Computes gene regulatory networks for subsamples of cells based on pri...
Performs non-linear manifold alignment of two gene regulatory networks...
Computes a gene regulatory network based on principal component regres...
Performs single-cell data quality control
scTenifoldNet
Performs CANDECOMP/PARAFAC (CP) Tensor Decomposition.
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.
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