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


BACKGROUND
RJaCGH is available as an R package from CRAN repositories (e.g. http://cran.r-project.org/web/packages).
Rueda
Oscar M
OM
Structural Biology and Biocomputing Programme, Spanish National Cancer Center (CNIO), Madrid 28029, Spain. rueda.om@gmail.com
Diaz-Uriarte
Ramon
R
D001499:Bayes Theorem; D055028:Comparative Genomic Hybridization / Q000379:methods*; D018628:Gene Dosage; D017422:Sequence Analysis, DNA / Q000379:methods