Integrated Analysis of Gene Expression and Gene Copy Number for Gene Shaving Based on ICA Approach

Microarray gene expression and array CGH (aCGH) are two genomic approaches, which are widely used for biomedical discovery. While microarray gene expression analysis provides functional information, the aCGH analysis provides structural variations of genome using gene copy number analysis. The integration of this complementary information is challenging. We proposed a method, named as "gene shaving", to identify subsets of the genes with coherent expression patterns and large variation across samples. We used 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 applied 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 investigated the properties of our proposed method by analyzing both simulated and real data. We demonstrated that the robustness of our method to noise using simulated data. Using breast cancer data, we showed that our method is superior to the Generalized Singular Value Decomposition (GSVD) gene shaving method for identifying genes associated with breast cancer.