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Patterns (New York, N.Y.)
Dec 11, 2020;1 (9): 100139.

scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data.

Daniel Osorio, Yan Zhong, Guanxun Li, Jianhua Z Huang, James J Cai
Author Information
  1. Daniel Osorio: Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA.
  2. Yan Zhong: Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
  3. Guanxun Li: Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
  4. Jianhua Z Huang: Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
  5. James J Cai: Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA.
PMID: 33336197 DOI: 10.1016/j.patter.2020.100139

Abstract

We present scTenifoldNet-a machine learning workflow built upon principal-component regression, low-rank tensor approximation, and manifold alignment-for constructing and comparing single-cell gene regulatory networks (scGRNs) using data from single-cell RNA sequencing. scTenifoldNet reveals regulatory changes in gene expression between samples by comparing the constructed scGRNs. With real data, scTenifoldNet identifies specific gene expression programs associated with different biological processes, providing critical insights into the underlying mechanism of regulatory networks governing cellular transcriptional activities.

Keywords

gene regulatory network machine learning manifold alignment principal-component regression scRNA-seq scTenifoldNet single-cell RNA sequencing tensor decomposition

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Journal Article
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