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dc.contributor.authorCassese, Alberto
Guindani, Michele
Vannucci, Marina
dc.date.accessioned 2014-12-01T17:26:01Z
dc.date.available 2014-12-01T17:26:01Z
dc.date.issued 2014
dc.identifier.citation Cassese, Alberto, Guindani, Michele and Vannucci, Marina. "A Bayesian Integrative Model for Genetical Genomics with Spatially Informed Variable Selection." Cancer Informatics, 13, no. S2 (2014) 29-37. http://dx.doi.org/10.4137/CIn.s13784.
dc.identifier.urihttp://hdl.handle.net/1911/78535
dc.description.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 incorpo-rates 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.
dc.language.iso eng
dc.rightshttps://http://
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/
dc.title A Bayesian Integrative Model for Genetical Genomics with Spatially Informed Variable Selection
dc.type Journal article
dc.citation.journalTitle Cancer Informatics
dc.subject.keywordBayesian hierarchical models
copy number variants
gene expression
measurement error
variable selection
dc.citation.volumeNumber 13
dc.citation.issueNumber S2
dc.contributor.publisher Libertas Academica
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.4137/CIn.s13784
dc.identifier.pmcid PMC4179607
dc.identifier.pmid 25288877
dc.type.publication publisher version
dc.citation.firstpage 29
dc.citation.lastpage 37


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Except where otherwise noted, this item's license is described as This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).