Integrative analysis of metabolomics and transcriptomics data: a unified model framework to identify underlying system pathways.

The abundance of high-dimensional measurements in the form of gene expression and mass spectroscopy calls for models to elucidate the underlying biological system. For widely studied organisms like yeast, it is possible to incorporate prior knowledge from a variety of databases, an approach used in...

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Autores principales:	Kasper Brink-Jensen, Søren Bak, Kirsten Jørgensen, Claus Thorn Ekstrøm
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e7f9f0aca7814acaa9098cb72b46df40
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spelling	oai:doaj.org-article:e7f9f0aca7814acaa9098cb72b46df402021-11-18T08:53:41ZIntegrative analysis of metabolomics and transcriptomics data: a unified model framework to identify underlying system pathways.1932-620310.1371/journal.pone.0072116https://doaj.org/article/e7f9f0aca7814acaa9098cb72b46df402013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24086255/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The abundance of high-dimensional measurements in the form of gene expression and mass spectroscopy calls for models to elucidate the underlying biological system. For widely studied organisms like yeast, it is possible to incorporate prior knowledge from a variety of databases, an approach used in several recent studies. However if such information is not available for a particular organism these methods fall short. In this paper we propose a statistical method that is applicable to a dataset consisting of Liquid Chromatography-Mass Spectroscopy (LC-MS) and gene expression (DNA microarray) measurements from the same samples, to identify genes controlling the production of metabolites. Due to the high dimensionality of both LC-MS and DNA microarray data, dimension reduction and variable selection are key elements of the analysis. Our proposed approach starts by identifying the basis functions ("building blocks") that constitute the output from a mass spectrometry experiment. Subsequently, the weights of these basis functions are related to the observations from the corresponding gene expression data in order to identify which genes are associated with specific patterns seen in the metabolite data. The modeling framework is extremely flexible as well as computationally fast and can accommodate treatment effects and other variables related to the experimental design. We demonstrate that within the proposed framework, genes regulating the production of specific metabolites can be identified correctly unless the variation in the noise is more than twice that of the signal.Kasper Brink-JensenSøren BakKirsten JørgensenClaus Thorn EkstrømPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 9, p e72116 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kasper Brink-Jensen Søren Bak Kirsten Jørgensen Claus Thorn Ekstrøm Integrative analysis of metabolomics and transcriptomics data: a unified model framework to identify underlying system pathways.
description	The abundance of high-dimensional measurements in the form of gene expression and mass spectroscopy calls for models to elucidate the underlying biological system. For widely studied organisms like yeast, it is possible to incorporate prior knowledge from a variety of databases, an approach used in several recent studies. However if such information is not available for a particular organism these methods fall short. In this paper we propose a statistical method that is applicable to a dataset consisting of Liquid Chromatography-Mass Spectroscopy (LC-MS) and gene expression (DNA microarray) measurements from the same samples, to identify genes controlling the production of metabolites. Due to the high dimensionality of both LC-MS and DNA microarray data, dimension reduction and variable selection are key elements of the analysis. Our proposed approach starts by identifying the basis functions ("building blocks") that constitute the output from a mass spectrometry experiment. Subsequently, the weights of these basis functions are related to the observations from the corresponding gene expression data in order to identify which genes are associated with specific patterns seen in the metabolite data. The modeling framework is extremely flexible as well as computationally fast and can accommodate treatment effects and other variables related to the experimental design. We demonstrate that within the proposed framework, genes regulating the production of specific metabolites can be identified correctly unless the variation in the noise is more than twice that of the signal.
format	article
author	Kasper Brink-Jensen Søren Bak Kirsten Jørgensen Claus Thorn Ekstrøm
author_facet	Kasper Brink-Jensen Søren Bak Kirsten Jørgensen Claus Thorn Ekstrøm
author_sort	Kasper Brink-Jensen
title	Integrative analysis of metabolomics and transcriptomics data: a unified model framework to identify underlying system pathways.
title_short	Integrative analysis of metabolomics and transcriptomics data: a unified model framework to identify underlying system pathways.
title_full	Integrative analysis of metabolomics and transcriptomics data: a unified model framework to identify underlying system pathways.
title_fullStr	Integrative analysis of metabolomics and transcriptomics data: a unified model framework to identify underlying system pathways.
title_full_unstemmed	Integrative analysis of metabolomics and transcriptomics data: a unified model framework to identify underlying system pathways.
title_sort	integrative analysis of metabolomics and transcriptomics data: a unified model framework to identify underlying system pathways.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/e7f9f0aca7814acaa9098cb72b46df40
work_keys_str_mv	AT kasperbrinkjensen integrativeanalysisofmetabolomicsandtranscriptomicsdataaunifiedmodelframeworktoidentifyunderlyingsystempathways AT sørenbak integrativeanalysisofmetabolomicsandtranscriptomicsdataaunifiedmodelframeworktoidentifyunderlyingsystempathways AT kirstenjørgensen integrativeanalysisofmetabolomicsandtranscriptomicsdataaunifiedmodelframeworktoidentifyunderlyingsystempathways AT clausthornekstrøm integrativeanalysisofmetabolomicsandtranscriptomicsdataaunifiedmodelframeworktoidentifyunderlyingsystempathways
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Integrative analysis of metabolomics and transcriptomics data: a unified model framework to identify underlying system pathways.

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