Comparative analysis of gene expression and DNA copy number data for pancreatic and breast cancers using an orthogonal decomposition

The causes of over-expression for many diseases are typically unknown, but current studies show that copy number aberrations may be strong candidates for driving gene overexpression. We present the use of the generalized singular value decomposition (GSVD) for simultaneously identifying relevant influences common to only copy numbers, gene expression, or both measurements in conjunction. These groups are reported and gene ontology (GO) annotations are used as a functional assessment of the groupings accompanied by probabilistic significance obtained by combinatorics. We illustrate this method for two independently published studies of pancreatic cancer and breast cancer, where public gene expression and DNA copy number data is provided and measured across numerous tumor cell lines.