Document Type

Article

Publication Date

2011

Abstract

Analyzing gene expression data at the gene set level greatly improves feature extraction and data interpretation. Currently most efforts in gene set analysis are focused on differential expression analysis - finding gene sets whose genes show first-order relationship with the clinical outcome. However the regulation of the biological system is complex, and much of the change in gene expression dynamics do not manifest in the form of differential expression. At the gene set level, capturing the change in expression dynamics is difficult due to the complexity and heterogeneity of the gene sets. Here we report a systematic approach to detect gene sets that show differential coordination patterns with the rest of the transcriptome, as well as pairs of gene sets that are differentially coordinated with each other. We demonstrate that the method can identify biologically relevant gene sets, many of which do not show first-order relationship with the clinical outcome.

Publication Title

Genomics

Volume

98

Issue

6

First Page

469

Last Page

477

Comments

This article was published in Genomics, Volume 98, Issue 6, Pages 469-477.

The published version is available at http://dx.doi.org/10.1016/j.ygeno.2011.09.001.

Copyright © 2011 Elsevier and licensed under the Elsevier User License.

Download

Included in

Genomics Commons

COinS