iBATCGH: Integrative Bayesian Analysis of Transcriptomic and CGH data

Alberto Cassese, Michele Guindani, Marina Vannucci

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


We describe a method for the integration of high-throughput data from different sources. More specifically, iBATCGH is a package for the integrative analysis of transcriptomic and genomic data, based on a hierarchical Bayesian model. Through the specification of a measurement error model we relate the gene expression levels to latent copy number states which, in turn, are related to the observed surrogate CGH measurement via a hidden Markov model. Selection of relevant associations is performed employing variable selection priors that explicitly incorporate dependence information across adjacent copy number states. Posterior inference is carried out through Markov chain Monte Carlo techniques that efficiently explores the space of all possible associations. In this Chapter we review the model and present the functions provided in iBATCGH, an R package based on a C implementation of the inferential algorithm. Lastly, we illustrate the method via a case study on ovarian cancer
Original languageEnglish
Title of host publicationStatistical Analysis for High-Dimensional Data
Subtitle of host publicationThe Abel Symposium 2014
EditorsFrigessi Arnoldo, Peter Buhlmann, Ingrid K. Glad, Mette Langaas, Sylvia Richardson, Marina Vannucci
PublisherSpringer Verlag
ISBN (Electronic)978-3-319-27099-9
ISBN (Print)978-3-319-27097-5
Publication statusPublished - 1 Feb 2016

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