A new mixture model approach to analyzing allelic-loss data using Bayes factors

Background: Allelic-loss studies record data on the loss of genetic material in tumor tissue relative to normal tissue at various loci along the genome. As the deletion of a tumor suppressor gene can lead to tumor development, one objective of these studies is to determine which, if any, chromosome arms harbor tumor suppressor genes. Results: We propose a large class of mixture models for describing the data, and we suggest using Bayes factors to select a reasonable model from the class in order to classify the chromosome arms. Bayes factors are especially useful in the case of testing that the number of components in a

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Manisha Desai, Mary J. Emond
• Keywords: Bayes Factors, BMCBI 2004, Chromosome Arms, Tumor Suppressor Genes