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Identification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy

A complex tissue contains a variety of cells with distinct molecular signatures. Single-cell RNA sequencing has characterized the transcriptomes of different cell types and enables researchers to discover the underlying mechanisms of cellular heterogeneity. A critical task in single-cell transcripto...

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Autores principales: Gan, Yanglan, Liang, Shanshan, Wei, Qingting, Zou, Guobing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649823/
https://www.ncbi.nlm.nih.gov/pubmed/33195248
http://dx.doi.org/10.3389/fcell.2020.588041
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author Gan, Yanglan
Liang, Shanshan
Wei, Qingting
Zou, Guobing
author_facet Gan, Yanglan
Liang, Shanshan
Wei, Qingting
Zou, Guobing
author_sort Gan, Yanglan
collection PubMed
description A complex tissue contains a variety of cells with distinct molecular signatures. Single-cell RNA sequencing has characterized the transcriptomes of different cell types and enables researchers to discover the underlying mechanisms of cellular heterogeneity. A critical task in single-cell transcriptome studies is to uncover transcriptional differences among specific cell types. However, the intercellular transcriptional variation is usually confounded with high level of technical noise, which masks the important biological signals. Here, we propose a new computational method DiffGE for differential analysis, adopting network entropy to measure the expression dynamics of gene groups among different cell types and to identify the highly differential gene groups. To evaluate the effectiveness of our proposed method, DiffGE is applied to three independent single-cell RNA-seq datasets and to identify the highly dynamic gene groups that exhibit distinctive expression patterns in different cell types. We compare the results of our method with those of three widely applied algorithms. Further, the gene function analysis indicates that these detected differential gene groups are significantly related to cellular regulation processes. The results demonstrate the power of our method in evaluating the transcriptional dynamics and identifying highly differential gene groups among different cell types.
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spelling pubmed-76498232020-11-13 Identification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy Gan, Yanglan Liang, Shanshan Wei, Qingting Zou, Guobing Front Cell Dev Biol Cell and Developmental Biology A complex tissue contains a variety of cells with distinct molecular signatures. Single-cell RNA sequencing has characterized the transcriptomes of different cell types and enables researchers to discover the underlying mechanisms of cellular heterogeneity. A critical task in single-cell transcriptome studies is to uncover transcriptional differences among specific cell types. However, the intercellular transcriptional variation is usually confounded with high level of technical noise, which masks the important biological signals. Here, we propose a new computational method DiffGE for differential analysis, adopting network entropy to measure the expression dynamics of gene groups among different cell types and to identify the highly differential gene groups. To evaluate the effectiveness of our proposed method, DiffGE is applied to three independent single-cell RNA-seq datasets and to identify the highly dynamic gene groups that exhibit distinctive expression patterns in different cell types. We compare the results of our method with those of three widely applied algorithms. Further, the gene function analysis indicates that these detected differential gene groups are significantly related to cellular regulation processes. The results demonstrate the power of our method in evaluating the transcriptional dynamics and identifying highly differential gene groups among different cell types. Frontiers Media S.A. 2020-10-22 /pmc/articles/PMC7649823/ /pubmed/33195248 http://dx.doi.org/10.3389/fcell.2020.588041 Text en Copyright © 2020 Gan, Liang, Wei and Zou. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cell and Developmental Biology
Gan, Yanglan
Liang, Shanshan
Wei, Qingting
Zou, Guobing
Identification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy
title Identification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy
title_full Identification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy
title_fullStr Identification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy
title_full_unstemmed Identification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy
title_short Identification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy
title_sort identification of differential gene groups from single-cell transcriptomes using network entropy
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649823/
https://www.ncbi.nlm.nih.gov/pubmed/33195248
http://dx.doi.org/10.3389/fcell.2020.588041
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