Cassese, AlbertoGuindani, MicheleVannucci, Marina2014-12-012014-12-012014Cassese, Alberto, Guindani, Michele and Vannucci, Marina. "A Bayesian Integrative Model for Genetical Genomics with Spatially Informed Variable Selection." <i>Cancer Informatics,</i> 13, no. S2 (2014) Libertas Academica: 29-37. http://dx.doi.org/10.4137/CIn.s13784.https://hdl.handle.net/1911/78535We 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.engThis 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).Journal articleBayesian hierarchical modelscopy number variantsgene expressionmeasurement errorvariable selectionhttp://dx.doi.org/10.4137/CIn.s13784