DNA copy number and gene expression data are complementary for deciphering the molecular-level mechanisms of cancer. In this paper, we use the Pollack lab pancreatic cancer dataset for determining a set of genes with consistent expression changes in pancreatic cancer and relate these genes with the DNA copy number changes observed in the individual samples. Our rank-covering algorithm can be used to obtain an aggregated view of the candidate genes with DCN alterations that potentially explain the observed phenotype. The algorithm recovered a set of genes with well-known roles in pancreatic cancer, but also suggested an additional promising set of genes, which seem to be involved in cancer, but whose exact role in pancreatic cancer remains to be determined experimentally.
bioinformatics, gene expression data analysis, DNA copy number data analysis.
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