Integration of gene expression and gene copy number variations with independent component analysis.

It is recognized that a biological system should be characterized with multiscale and multi-modality imaging platforms such as using microarray gene expression and array CGH. While microarray gene expression analysis presents functional information, the array CGH analysis provides structural variations of genome using gene copy number analysis. The integration of this complementary information is challenging. We view the gene expression and copy number variations as two different measurements of a biological system and apply the independent component analysis (ICA) to project the data into statistically independent biological processes, which are then integrated to identify variation patterns in two inputs. We apply the method to cluster group of genes, resulting better identification of genes that are statistically significant in both measurements (e.g., gene expression and aCGH). We also compared the approach with other approaches such as principal component analysis (PCA), and generalized singular value decomposition (GSVD), demonstrating improved performance in 'gene shaving'.