Cargando…

MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis

A phenotype may be associated with multiple genes that interact with each other in the form of a gene module or network. How to identify these relationships is one important aspect of comparative transcriptomics. However, it is still a challenge to align gene modules associated with different phenot...

Descripción completa

Detalles Bibliográficos
Autores principales: Cai, Guoxin, Zhao, Wenyi, Zhou, Zhan, Gu, Xun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359084/
https://www.ncbi.nlm.nih.gov/pubmed/37279601
http://dx.doi.org/10.1093/bib/bbad207
_version_ 1785075803984232448
author Cai, Guoxin
Zhao, Wenyi
Zhou, Zhan
Gu, Xun
author_facet Cai, Guoxin
Zhao, Wenyi
Zhou, Zhan
Gu, Xun
author_sort Cai, Guoxin
collection PubMed
description A phenotype may be associated with multiple genes that interact with each other in the form of a gene module or network. How to identify these relationships is one important aspect of comparative transcriptomics. However, it is still a challenge to align gene modules associated with different phenotypes. Although several studies attempted to address this issue in different aspects, a general framework is still needed. In this study, we introduce Module Alignment of TranscripTomE (MATTE), a novel approach to analyze transcriptomics data and identify differences in a modular manner. MATTE assumes that gene interactions modulate a phenotype and models phenotype differences as gene location changes. Specifically, we first represented genes by a relative differential expression to reduce the influence of noise in omics data. Meanwhile, clustering and aligning are combined to depict gene differences in a modular way robustly. The results show that MATTE outperformed state-of-the-art methods in identifying differentially expressed genes under noise in gene expression. In particular, MATTE could also deal with single-cell ribonucleic acid-seq data to extract the best cell-type marker genes compared to other methods. Additionally, we demonstrate how MATTE supports the discovery of biologically significant genes and modules, and facilitates downstream analyses to gain insight into breast cancer. The source code of MATTE and case analysis are available at https://github.com/zjupgx/MATTE.
format Online
Article
Text
id pubmed-10359084
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-103590842023-07-21 MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis Cai, Guoxin Zhao, Wenyi Zhou, Zhan Gu, Xun Brief Bioinform Problem Solving Protocol A phenotype may be associated with multiple genes that interact with each other in the form of a gene module or network. How to identify these relationships is one important aspect of comparative transcriptomics. However, it is still a challenge to align gene modules associated with different phenotypes. Although several studies attempted to address this issue in different aspects, a general framework is still needed. In this study, we introduce Module Alignment of TranscripTomE (MATTE), a novel approach to analyze transcriptomics data and identify differences in a modular manner. MATTE assumes that gene interactions modulate a phenotype and models phenotype differences as gene location changes. Specifically, we first represented genes by a relative differential expression to reduce the influence of noise in omics data. Meanwhile, clustering and aligning are combined to depict gene differences in a modular way robustly. The results show that MATTE outperformed state-of-the-art methods in identifying differentially expressed genes under noise in gene expression. In particular, MATTE could also deal with single-cell ribonucleic acid-seq data to extract the best cell-type marker genes compared to other methods. Additionally, we demonstrate how MATTE supports the discovery of biologically significant genes and modules, and facilitates downstream analyses to gain insight into breast cancer. The source code of MATTE and case analysis are available at https://github.com/zjupgx/MATTE. Oxford University Press 2023-06-03 /pmc/articles/PMC10359084/ /pubmed/37279601 http://dx.doi.org/10.1093/bib/bbad207 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Problem Solving Protocol
Cai, Guoxin
Zhao, Wenyi
Zhou, Zhan
Gu, Xun
MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis
title MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis
title_full MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis
title_fullStr MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis
title_full_unstemmed MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis
title_short MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis
title_sort matte: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis
topic Problem Solving Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10359084/
https://www.ncbi.nlm.nih.gov/pubmed/37279601
http://dx.doi.org/10.1093/bib/bbad207
work_keys_str_mv AT caiguoxin matteapipelineoftranscriptomemodulealignmentforantinoisephenotypegenerelatedanalysis
AT zhaowenyi matteapipelineoftranscriptomemodulealignmentforantinoisephenotypegenerelatedanalysis
AT zhouzhan matteapipelineoftranscriptomemodulealignmentforantinoisephenotypegenerelatedanalysis
AT guxun matteapipelineoftranscriptomemodulealignmentforantinoisephenotypegenerelatedanalysis