Capturing Changes in Gene Expression Dynamics by Gene Set Differential Coordination Analysis

• Type of Material: Article
• Authors: Tianwei Yu, Emory University; Yun Bai, Philadelphia College of Osteopathic Medicine
• Language: English
• Date: 2011-12
• Publisher: Elsevier: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2011 Elsevier Inc. All rights reserved.
• License: Creative Commons Attribution-NonCommerical-NoDerivs 3.0 Unported
• Final Published Version (URL): http://dx.doi.org/10.1016/j.ygeno.2011.09.001
• Title of Journal or Parent Work: Genomics
• ISSN: 0888-7543
• Volume: 98
• Issue: 6
• Start Page: 469
• End Page: 477
• Grant/Funding Information: This research was partially supported by NIH grants 1P01ES016731-01, 2U19AI057266-06, 5P30AI50409-10 and 1UL1RR025008-02.
• Keywords: gene set analysis; gene expression; microarray
• Research Categories: Biology, Genetics; Biology, Biostatistics

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.