RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions
نویسندگان
چکیده
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).
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A flexible, accurate, and extensible statistical method for detecting genomic copy-number changes using Hidden Markov Models with Reversible Jump MCMC
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