Department of Biostatistics Seminar/Workshop Series

Supervised Principal Component Analysis for Gene Set Enrichment of Microarray Data

Xi (Steven) Chen, PhD

The Cleveland Clinic Foundation
Cleveland, Ohio

Wednesday, September 10, 1:45-2:55pm, MRBIII Conference Room 1220

Intended Audience: Persons interested in applied statistics, statistical theory, epidemiology, health services research, clinical trials methodology, statistical computing, statistical graphics, R users or potential users

Gene set analysis allows formal testing of subtle but coordinated changes in a group of genes, such as those defined by Gene Ontology or KEGG Pathway databases. We propose a new method for gene set analysis that is based on Principal Component Analysis of genes expression values in the gene set. PCA is an effective method for reducing high dimensionality and capture variations in gene expression values. However, one limitation with PCA is that the latent variable identified by the first principal component may be unrelated to outcome. In the proposed Supervised Principal Component (SPCA) model for gene set analysis, the principal components are estimated from a selected subset of genes that are associated with outcome. As outcome information is used in the gene selection step, this method is supervised, thus called the Supervised PCA model. Because of the gene selection step, test statistic in SPCA model can no longer be approximated well using t distribution. We propose a two-component mixture distribution based on Gumbel extreme value distributions to account for the gene selection step. We show the proposed method compares favorably to currently available gene set analysis methods using simulated and real microarray data.