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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

Summary

This summary is machine-generated.

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