ArticleBioinformatics (Oxford, England)2009
RJaCGH: Bayesian analysis of aCGH arrays for detecting copy number changes and recurrent regions.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Integrative analysis of copy number and gene expression in breast cancer using formalin-fixed paraffin-embedded core biopsy tissue: a feasibility study.BMC genomics · 2017Article
- Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression.PLoS computational biology · 2016Article
- ParseCNV integrative copy number variation association software with quality tracking.Nucleic acids research · 2013Article
- Fast detection of de novo copy number variants from SNP arrays for case-parent trios.BMC bioinformatics · 2012Article
- O-miner: an integrative platform for automated analysis and mining of -omics data.Nucleic acids research · 2012Article
- Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana.PLoS computational biology · 2012Article
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Authors and funding
2 authors.
Funding
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Abstract
summarySeveral 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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