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

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 regulat...

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Detalles Bibliográficos
Autores principales: Osorio, Daniel, Zhong, Yan, Li, Guanxun, Huang, Jianhua Z., Cai, James J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Descriptor
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733883/
https://www.ncbi.nlm.nih.gov/pubmed/33336197
http://dx.doi.org/10.1016/j.patter.2020.100139
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author Osorio, Daniel
Zhong, Yan
Li, Guanxun
Huang, Jianhua Z.
Cai, James J.
author_facet Osorio, Daniel
Zhong, Yan
Li, Guanxun
Huang, Jianhua Z.
Cai, James J.
author_sort Osorio, Daniel
collection PubMed
description 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.
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spelling pubmed-77338832020-12-16 scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data Osorio, Daniel Zhong, Yan Li, Guanxun Huang, Jianhua Z. Cai, James J. Patterns (N Y) Descriptor 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. Elsevier 2020-11-05 /pmc/articles/PMC7733883/ /pubmed/33336197 http://dx.doi.org/10.1016/j.patter.2020.100139 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Descriptor
Osorio, Daniel
Zhong, Yan
Li, Guanxun
Huang, Jianhua Z.
Cai, James J.
scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data
title scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data
title_full scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data
title_fullStr scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data
title_full_unstemmed scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data
title_short scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data
title_sort sctenifoldnet: a machine learning workflow for constructing and comparing transcriptome-wide gene regulatory networks from single-cell data
topic Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733883/
https://www.ncbi.nlm.nih.gov/pubmed/33336197
http://dx.doi.org/10.1016/j.patter.2020.100139
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