De-correlating expression in gene-set analysis.

ABSTRACT:

Motivation

Group-wise pattern analysis of genes, known as gene-set analysis (GSA), addresses the differential expression pattern of biologically pre-defined gene sets. GSA exhibits high statistical power and has revealed many novel biological processes associated with specific phenotypes. In most cases, however, GSA relies on the invalid assumption that the members of each gene set are sampled independently, which increases false predictions.

Results

We propose an algorithm, termed DECO, to remove (or alleviate) the bias caused by the correlation of the expression data in GSAs. This is accomplished through the eigenvalue-decomposition of covariance matrixes and a series of linear transformations of data. In particular, moderate de-correlation methods that truncate or re-scal

SUBMITTER: Nam D 

PROVIDER: S-EPMC2935420 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature