Simplivariate models: uncovering the underlying biology in functional genomics data.

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simp...

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Autores principales:	Edoardo Saccenti, Johan A Westerhuis, Age K Smilde, Mariët J van der Werf, Jos A Hageman, Margriet M W B Hendriks
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bb15782ee0bd4a54b84c243b5cfc4c75
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id	oai:doaj.org-article:bb15782ee0bd4a54b84c243b5cfc4c75
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spelling	oai:doaj.org-article:bb15782ee0bd4a54b84c243b5cfc4c752021-11-18T06:51:52ZSimplivariate models: uncovering the underlying biology in functional genomics data.1932-620310.1371/journal.pone.0020747https://doaj.org/article/bb15782ee0bd4a54b84c243b5cfc4c752011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21698241/?tool=EBIhttps://doaj.org/toc/1932-6203One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components.We present a new implementation of the recently introduced simplivariate models. In this implementation, the informative variation is described by multiplicative models that can adequately represent the relations between functional genomics data. Both a simulated and two real-life metabolomics data sets show good performance of the method.Edoardo SaccentiJohan A WesterhuisAge K SmildeMariët J van der WerfJos A HagemanMargriet M W B HendriksPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 6, p e20747 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Edoardo Saccenti Johan A Westerhuis Age K Smilde Mariët J van der Werf Jos A Hageman Margriet M W B Hendriks Simplivariate models: uncovering the underlying biology in functional genomics data.
description	One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components.We present a new implementation of the recently introduced simplivariate models. In this implementation, the informative variation is described by multiplicative models that can adequately represent the relations between functional genomics data. Both a simulated and two real-life metabolomics data sets show good performance of the method.
format	article
author	Edoardo Saccenti Johan A Westerhuis Age K Smilde Mariët J van der Werf Jos A Hageman Margriet M W B Hendriks
author_facet	Edoardo Saccenti Johan A Westerhuis Age K Smilde Mariët J van der Werf Jos A Hageman Margriet M W B Hendriks
author_sort	Edoardo Saccenti
title	Simplivariate models: uncovering the underlying biology in functional genomics data.
title_short	Simplivariate models: uncovering the underlying biology in functional genomics data.
title_full	Simplivariate models: uncovering the underlying biology in functional genomics data.
title_fullStr	Simplivariate models: uncovering the underlying biology in functional genomics data.
title_full_unstemmed	Simplivariate models: uncovering the underlying biology in functional genomics data.
title_sort	simplivariate models: uncovering the underlying biology in functional genomics data.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/bb15782ee0bd4a54b84c243b5cfc4c75
work_keys_str_mv	AT edoardosaccenti simplivariatemodelsuncoveringtheunderlyingbiologyinfunctionalgenomicsdata AT johanawesterhuis simplivariatemodelsuncoveringtheunderlyingbiologyinfunctionalgenomicsdata AT ageksmilde simplivariatemodelsuncoveringtheunderlyingbiologyinfunctionalgenomicsdata AT marietjvanderwerf simplivariatemodelsuncoveringtheunderlyingbiologyinfunctionalgenomicsdata AT josahageman simplivariatemodelsuncoveringtheunderlyingbiologyinfunctionalgenomicsdata AT margrietmwbhendriks simplivariatemodelsuncoveringtheunderlyingbiologyinfunctionalgenomicsdata
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Simplivariate models: uncovering the underlying biology in functional genomics data.

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