Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data.

We consider modeling jointly microarray RNA expression and DNA copy number data. We propose Bayesian mixture models that define latent Gaussian probit scores for the DNA and RNA, and integrate between the two platforms via a regression of the RNA probit scores on the DNA probit scores. Such a regres...

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Main Authors:	Filippo Trentini, Yuan Ji, Takayuki Iwamoto, Yuan Qi, Lajos Pusztai, Peter Müller
Format:	Article
Language:	English
Published:	Public Library of Science (PLoS) 2013-01-01
Series:	PLoS ONE
Online Access:	http://europepmc.org/articles/PMC3709899?pdf=render

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spelling	doaj-e7a9d30637374f43822c18104ca853772020-11-25T02:15:26ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0187e6807110.1371/journal.pone.0068071Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data.Filippo TrentiniYuan JiTakayuki IwamotoYuan QiLajos PusztaiPeter MüllerWe consider modeling jointly microarray RNA expression and DNA copy number data. We propose Bayesian mixture models that define latent Gaussian probit scores for the DNA and RNA, and integrate between the two platforms via a regression of the RNA probit scores on the DNA probit scores. Such a regression conveniently allows us to include additional sample specific covariates such as biological conditions and clinical outcomes. The two developed methods are aimed respectively to make inference on differential behaviour of genes in patients showing different subtypes of breast cancer and to predict the pathological complete response (pCR) of patients borrowing strength across the genomic platforms. Posterior inference is carried out via MCMC simulations. We demonstrate the proposed methodology using a published data set consisting of 121 breast cancer patients.http://europepmc.org/articles/PMC3709899?pdf=render
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Filippo Trentini Yuan Ji Takayuki Iwamoto Yuan Qi Lajos Pusztai Peter Müller
spellingShingle	Filippo Trentini Yuan Ji Takayuki Iwamoto Yuan Qi Lajos Pusztai Peter Müller Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data. PLoS ONE
author_facet	Filippo Trentini Yuan Ji Takayuki Iwamoto Yuan Qi Lajos Pusztai Peter Müller
author_sort	Filippo Trentini
title	Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data.
title_short	Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data.
title_full	Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data.
title_fullStr	Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data.
title_full_unstemmed	Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data.
title_sort	bayesian mixture models for assessment of gene differential behaviour and prediction of pcr through the integration of copy number and gene expression data.
publisher	Public Library of Science (PLoS)
series	PLoS ONE
issn	1932-6203
publishDate	2013-01-01
description	We consider modeling jointly microarray RNA expression and DNA copy number data. We propose Bayesian mixture models that define latent Gaussian probit scores for the DNA and RNA, and integrate between the two platforms via a regression of the RNA probit scores on the DNA probit scores. Such a regression conveniently allows us to include additional sample specific covariates such as biological conditions and clinical outcomes. The two developed methods are aimed respectively to make inference on differential behaviour of genes in patients showing different subtypes of breast cancer and to predict the pathological complete response (pCR) of patients borrowing strength across the genomic platforms. Posterior inference is carried out via MCMC simulations. We demonstrate the proposed methodology using a published data set consisting of 121 breast cancer patients.
url	http://europepmc.org/articles/PMC3709899?pdf=render
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Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data.

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