Journal Article

RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions

Oscar M. Rueda and Ramon Diaz-Uriarte

in Bioinformatics

Volume 25, issue 15, pages 1959-1960
Published in print August 2009 | ISSN: 1367-4803
Published online May 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp307

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Summary: Several methods have been proposed to detect copy number changes and recurrent regions of copy number variation from aCGH, but few methods return probabilities of alteration explicitly, which are the direct answer to the question ‘is this probe/region altered?’ RJaCGH fits a Non-Homogeneous Hidden Markov model to the aCGH data using Markov Chain Monte Carlo with Reversible Jump, and returns the probability that each probe is gained or lost. Using these probabilites, recurrent regions (over sets of individuals) of copy number alteration can be found.

Availability: RJaCGH is available as an R package from CRAN repositories (e.g. http://cran.r-project.org/web/packages).

Contact: rueda.om@gmail.com; rueda.om@gmail.com

Journal Article.  1484 words. 

Subjects: Bioinformatics and Computational Biology

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