Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study

Abstract A gene co-expression network (GCN) describes associations between genes and points to genetic coordination of biochemical pathways. However, genetic correlations in a GCN are only detectable if they are present in the sampled conditions. With the increasing quantity of gene expression sampl...

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Autores principales:	Stephen P. Ficklin, Leland J. Dunwoodie, William L. Poehlman, Christopher Watson, Kimberly E. Roche, F. Alex Feltus
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4cac819eb2434b86901fb10baff65daf
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spelling	oai:doaj.org-article:4cac819eb2434b86901fb10baff65daf2021-12-02T11:52:40ZDiscovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study10.1038/s41598-017-09094-42045-2322https://doaj.org/article/4cac819eb2434b86901fb10baff65daf2017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-09094-4https://doaj.org/toc/2045-2322Abstract A gene co-expression network (GCN) describes associations between genes and points to genetic coordination of biochemical pathways. However, genetic correlations in a GCN are only detectable if they are present in the sampled conditions. With the increasing quantity of gene expression samples available in public repositories, there is greater potential for discovery of genetic correlations from a variety of biologically interesting conditions. However, even if gene correlations are present, their discovery can be masked by noise. Noise is introduced from natural variation (intrinsic and extrinsic), systematic variation (caused by sample measurement protocols and instruments), and algorithmic and statistical variation created by selection of data processing tools. A variety of published studies, approaches and methods attempt to address each of these contributions of variation to reduce noise. Here we describe an approach using Gaussian Mixture Models (GMMs) to address natural extrinsic (condition-specific) variation during network construction from mixed input conditions. To demonstrate utility, we build and analyze a condition-annotated GCN from a compendium of 2,016 mixed gene expression data sets from five tumor subtypes obtained from The Cancer Genome Atlas. Our results show that GMMs help discover tumor subtype specific gene co-expression patterns (modules) that are significantly enriched for clinical attributes.Stephen P. FicklinLeland J. DunwoodieWilliam L. PoehlmanChristopher WatsonKimberly E. RocheF. Alex FeltusNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Stephen P. Ficklin Leland J. Dunwoodie William L. Poehlman Christopher Watson Kimberly E. Roche F. Alex Feltus Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study
description	Abstract A gene co-expression network (GCN) describes associations between genes and points to genetic coordination of biochemical pathways. However, genetic correlations in a GCN are only detectable if they are present in the sampled conditions. With the increasing quantity of gene expression samples available in public repositories, there is greater potential for discovery of genetic correlations from a variety of biologically interesting conditions. However, even if gene correlations are present, their discovery can be masked by noise. Noise is introduced from natural variation (intrinsic and extrinsic), systematic variation (caused by sample measurement protocols and instruments), and algorithmic and statistical variation created by selection of data processing tools. A variety of published studies, approaches and methods attempt to address each of these contributions of variation to reduce noise. Here we describe an approach using Gaussian Mixture Models (GMMs) to address natural extrinsic (condition-specific) variation during network construction from mixed input conditions. To demonstrate utility, we build and analyze a condition-annotated GCN from a compendium of 2,016 mixed gene expression data sets from five tumor subtypes obtained from The Cancer Genome Atlas. Our results show that GMMs help discover tumor subtype specific gene co-expression patterns (modules) that are significantly enriched for clinical attributes.
format	article
author	Stephen P. Ficklin Leland J. Dunwoodie William L. Poehlman Christopher Watson Kimberly E. Roche F. Alex Feltus
author_facet	Stephen P. Ficklin Leland J. Dunwoodie William L. Poehlman Christopher Watson Kimberly E. Roche F. Alex Feltus
author_sort	Stephen P. Ficklin
title	Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study
title_short	Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study
title_full	Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study
title_fullStr	Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study
title_full_unstemmed	Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study
title_sort	discovering condition-specific gene co-expression patterns using gaussian mixture models: a cancer case study
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/4cac819eb2434b86901fb10baff65daf
work_keys_str_mv	AT stephenpficklin discoveringconditionspecificgenecoexpressionpatternsusinggaussianmixturemodelsacancercasestudy AT lelandjdunwoodie discoveringconditionspecificgenecoexpressionpatternsusinggaussianmixturemodelsacancercasestudy AT williamlpoehlman discoveringconditionspecificgenecoexpressionpatternsusinggaussianmixturemodelsacancercasestudy AT christopherwatson discoveringconditionspecificgenecoexpressionpatternsusinggaussianmixturemodelsacancercasestudy AT kimberlyeroche discoveringconditionspecificgenecoexpressionpatternsusinggaussianmixturemodelsacancercasestudy AT falexfeltus discoveringconditionspecificgenecoexpressionpatternsusinggaussianmixturemodelsacancercasestudy
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Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study

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