A Bayesian Approach for Integrated Cancer Genome Profiling

Microarrays allow one to simultaneously measure the expression of tens of thousands of genes in human (cancer) cells, and recently also the copy-number at a hundred-thousand different positions on the genomic DNA. The data, for instance, contains information on cancer relevant genes and types of cancer. But the enormous amount of data has to be appropriately processed and integrated in order to allow the extraction of the relevant information. Novel statistical methods have to be developed that are able to cope with such complex data, yet small sample sizes. The Bayesian approach is particularly suited for this problem. The goal of the PhD project is to develop an integrative Bayesian approach to microarray data analysis, prove its consistency, implement and apply it to the data sets of IOSI, and compare it to other methods.