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A Bayesian integrative model for genetical genomics with spatially informed variable selection

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A Bayesian integrative model for genetical genomics with spatially informed variable selection. / Cassese, Alberto; Guindani, Michele; Vannucci, Marina.

In: Cancer Informatics, Vol. 13, No. S2, 21.09.2014, p. 29-37.

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@article{2c4e420094e64b79811bbf2ab492612d,
title = "A Bayesian integrative model for genetical genomics with spatially informed variable selection",
abstract = "We consider a Bayesian hierarchical model for the integration of gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. The approach defines a measurement error model that relates the gene expression levels to latent copy number states. In turn, the latent states are related to the observed surrogate CGH measurements via a hidden Markov model. The model further incorporates variable selection with a spatial prior based on a probit link that exploits dependencies across adjacent DNA segments. Posterior inference is carried out via Markov chain Monte Carlo stochastic search techniques. We study the performance of the model in simulations and show better results than those achieved with recently proposed alternative priors. We also show an application to data from a genomic study on lung squamous cell carcinoma, where we identify potential candidates of associations between copy number variants and the transcriptional activity of target genes. Gene ontology (GO) analyses of our findings reveal enrichments in genes that code for proteins involved in cancer. Our model also identifies a number of potential candidate biomarkers for further experimental validation.",
keywords = "Bayesian hierarchical models, Copy number variants, Gene expression, Measurement error, Variable selection",
author = "Alberto Cassese and Michele Guindani and Marina Vannucci",
year = "2014",
month = "9",
day = "21",
doi = "10.4137/CIN.S13784",
language = "English",
volume = "13",
pages = "29--37",
journal = "Cancer Informatics",
issn = "1176-9351",
publisher = "SAGE Publications Ltd",
number = "S2",

}

RIS

TY - JOUR

T1 - A Bayesian integrative model for genetical genomics with spatially informed variable selection

AU - Cassese, Alberto

AU - Guindani, Michele

AU - Vannucci, Marina

PY - 2014/9/21

Y1 - 2014/9/21

N2 - We consider a Bayesian hierarchical model for the integration of gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. The approach defines a measurement error model that relates the gene expression levels to latent copy number states. In turn, the latent states are related to the observed surrogate CGH measurements via a hidden Markov model. The model further incorporates variable selection with a spatial prior based on a probit link that exploits dependencies across adjacent DNA segments. Posterior inference is carried out via Markov chain Monte Carlo stochastic search techniques. We study the performance of the model in simulations and show better results than those achieved with recently proposed alternative priors. We also show an application to data from a genomic study on lung squamous cell carcinoma, where we identify potential candidates of associations between copy number variants and the transcriptional activity of target genes. Gene ontology (GO) analyses of our findings reveal enrichments in genes that code for proteins involved in cancer. Our model also identifies a number of potential candidate biomarkers for further experimental validation.

AB - We consider a Bayesian hierarchical model for the integration of gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. The approach defines a measurement error model that relates the gene expression levels to latent copy number states. In turn, the latent states are related to the observed surrogate CGH measurements via a hidden Markov model. The model further incorporates variable selection with a spatial prior based on a probit link that exploits dependencies across adjacent DNA segments. Posterior inference is carried out via Markov chain Monte Carlo stochastic search techniques. We study the performance of the model in simulations and show better results than those achieved with recently proposed alternative priors. We also show an application to data from a genomic study on lung squamous cell carcinoma, where we identify potential candidates of associations between copy number variants and the transcriptional activity of target genes. Gene ontology (GO) analyses of our findings reveal enrichments in genes that code for proteins involved in cancer. Our model also identifies a number of potential candidate biomarkers for further experimental validation.

KW - Bayesian hierarchical models

KW - Copy number variants

KW - Gene expression

KW - Measurement error

KW - Variable selection

U2 - 10.4137/CIN.S13784

DO - 10.4137/CIN.S13784

M3 - Article

VL - 13

SP - 29

EP - 37

JO - Cancer Informatics

T2 - Cancer Informatics

JF - Cancer Informatics

SN - 1176-9351

IS - S2

ER -