alexa Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model.
Biomedical Sciences

Biomedical Sciences

International Journal of Biomedical Data Mining

Author(s): Brot P, Richardson S

Abstract Share this page

Abstract MOTIVATION: Comparative genomic hybridization array experiments that investigate gene copy number changes present new challenges for statistical analysis and call for methods that incorporate spatial dependence between sequences along the chromosome. For this purpose, we propose a novel method called CGHmix. It is based on a spatially structured mixture model with three states corresponding to genomic sequences that are either unmodified, deleted or amplified. Inference is performed in a Bayesian framework. From the output, posterior probabilities of belonging to each of the three states are estimated for each genomic sequence and used to classify them. RESULTS: Using simulated data, CGHmix is validated and compared with both a conventional unstructured mixture model and with a recently proposed data mining method. We demonstrate the good performance of CGHmix for classifying copy number changes. In addition, the method provides a good estimate of the false discovery rate. We also present the analysis of a cancer related dataset. SUPPLEMENTARY INFORMATION: This article was published in Bioinformatics and referenced in International Journal of Biomedical Data Mining

Relevant Expert PPTs

Relevant Speaker PPTs

Recommended Conferences

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version