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

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.

authors

• Cai, James

published proceedings

• Patterns (N Y)

altmetric score

• 16.196

author list (cited authors)

• Osorio, D., Zhong, Y., Li, G., Huang, J. Z., & Cai, J. J.

citation count

• 14

complete list of authors

• Osorio, Daniel||Zhong, Yan||Li, Guanxun||Huang, Jianhua Z||Cai, James J

publication date

• January 2020

publisher

• Elsevier  Publisher

published in

• Patterns  Journal

keywords

• Gene Regulatory Network
• Machine Learning
• Manifold Alignment
• Principal-component Regression
• Scrna-seq
• Sctenifoldnet
• Single-cell Rna Sequencing
• Tensor Decomposition

Digital Object Identifier (DOI)

• 10.1016/j.patter.2020.100139

start page

• 100139

end page

• 100139

volume

• 1

issue

• 9

URL

• http://dx.doi.org/10.1016/j.patter.2020.100139