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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.
Citation | scTenifoldNet citation info |
github.com/cailab-tamu/scTenifoldNet | |
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