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scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks

Daniel Osorio1, Yan Zhong2, Guanxun Li2

  • 1Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA.

Patterns (New York, N.Y.)
|December 18, 2020
PubMed
Summary

scTenifoldNet is a new machine learning tool that builds and compares single-cell gene regulatory networks (scGRNs) from single-cell RNA sequencing data to reveal gene expression changes and biological mechanisms.

Keywords:
gene regulatory networkmachine learningmanifold alignmentprincipal-component regressionscRNA-seqscTenifoldNetsingle-cell RNA sequencingtensor decomposition

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity.
  • Understanding gene regulatory networks (GRNs) is crucial for deciphering cellular functions.
  • Existing methods for constructing scGRNs face challenges in scalability and accuracy.

Purpose of the Study:

  • To introduce scTenifoldNet, a novel machine learning workflow for constructing and comparing single-cell gene regulatory networks (scGRNs).
  • To enable the identification of regulatory changes in gene expression between different biological samples.
  • To provide insights into the mechanisms governing cellular transcriptional activities.

Main Methods:

  • scTenifoldNet utilizes a combination of principal-component regression, low-rank tensor approximation, and manifold alignment.
  • The workflow processes scRNA-seq data to infer gene regulatory relationships.
  • Network comparison is performed to identify differential gene expression programs.

Main Results:

  • scTenifoldNet successfully constructs and compares scGRNs from scRNA-seq data.
  • The method reveals significant regulatory changes in gene expression across different samples.
  • Specific gene expression programs associated with distinct biological processes were identified.

Conclusions:

  • scTenifoldNet offers a robust computational approach for analyzing scGRNs.
  • The workflow provides critical insights into the regulatory mechanisms underlying cellular transcriptional dynamics.
  • This tool facilitates the comparison of gene regulatory landscapes across various biological contexts.