Supervised principal component analysis for gene set enrichment of microarray data with continuous or survival outcomes.

ABSTRACT:

Motivation

Gene set analysis allows formal testing of subtle but coordinated changes in a group of genes, such as those defined by Gene Ontology (GO) or KEGG Pathway databases. We propose a new method for gene set analysis that is based on principal component analysis (PCA) 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 PC may be unrelated to outcome.

Results

In the proposed supervised PCA (SPCA) model for gene set analysis, the PCs 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 su

SUBMITTER: Chen X 

PROVIDER: S-EPMC2732277 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature