ArticlePLoS computational biology2007
Flexible and accurate detection of genomic copy-number changes from aCGH.
Article in PLoS computational biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 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
- PSE-HMM: genome-wide CNV detection from NGS data using an HMM with Position-Specific Emission probabilities.BMC bioinformatics · 2016Article
- Limiting replication stress during somatic cell reprogramming reduces genomic instability in induced pluripotent stem cells.Nature communications · 2015Article
- Analysis of structural diversity in wolf-like canids reveals post-domestication variants.BMC genomics · 2014Article
- AbsCN-seq: a statistical method to estimate tumor purity, ploidy and absolute copy numbers from next-generation sequencing data.Bioinformatics (Oxford, England) · 2014Article
- Copy number variation genotyping using family information.BMC bioinformatics · 2013Article
- Fast detection of de novo copy number variants from SNP arrays for case-parent trios.BMC bioinformatics · 2012Article
- Parsimonious higher-order hidden Markov models for improved array-CGH analysis with applications to Arabidopsis thaliana.PLoS computational biology · 2012Article
- A high-resolution integrated map of copy number polymorphisms within and between breeds of the modern domesticated dog.BMC genomics · 2011Article
- Model-integrated estimation of normal tissue contamination for cancer SNP allelic copy number data.Cancer informatics · 2011Article
- Bayesian Frequentist hybrid Model wth Application to the Analysis of Gene Copy Number Changes.Journal of applied statistics · 2011Article
- Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays.BMC research notes · 2010Article
- Detection of copy number variation from array intensity and sequencing read depth using a stepwise Bayesian model.BMC bioinformatics · 2010Article
- Segmentation and estimation for SNP microarrays: a Bayesian multiple change-point approach.Biometrics · 2010Article
- Computational analysis of whole-genome differential allelic expression data in human.PLoS computational biology · 2010Article
- Identification of recurrent regions of Copy-Number Variants across multiple individuals.BMC bioinformatics · 2010Article
- A double-layered mixture model for the joint analysis of DNA copy number and gene expression data.Journal of computational biology : a journal of computational molecular cell biology · 2010Article
- A bayesian analysis for identifying DNA copy number variations using a compound poisson process.EURASIP journal on bioinformatics & systems biology · 2010Article
- PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data.Biostatistics (Oxford, England) · 2010Article
- Cancer gene discovery in mouse and man.Biochimica et biophysica acta · 2009Review
Corrections and comments
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Genomic DNA copy-number alterations (CNAs) are associated with complex diseases, including cancer: CNAs are indeed related to tumoral grade, metastasis, and patient survival. CNAs discovered from array-based comparative genomic hybridization (aCGH) data have been instrumental in identifying disease-related genes and potential therapeutic targets. To be immediately useful in both clinical and basic research scenarios, aCGH data analysis requires accurate methods that do not impose unrealistic biological assumptions and that provide direct answers to the key question, "What is the probability that this gene/region has CNAs?" Current approaches fail, however, to meet these requirements. Here, we introduce reversible jump aCGH (RJaCGH), a new method for identifying CNAs from aCGH; we use a nonhomogeneous hidden Markov model fitted via reversible jump Markov chain Monte Carlo; and we incorporate model uncertainty through Bayesian model averaging. RJaCGH provides an estimate of the probability that a gene/region has CNAs while incorporating interprobe distance and the capability to analyze data on a chromosome or genome-wide basis. RJaCGH outperforms alternative methods, and the performance difference is even larger with noisy data and highly variable interprobe distance, both commonly found features in aCGH data. Furthermore, our probabilistic method allows us to identify minimal common regions of CNAs among samples and can be extended to incorporate expression data. In summary, we provide a rigorous statistical framework for locating genes and chromosomal regions with CNAs with potential applications to cancer and other complex human diseases.
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