title: Capturing changes in gene expression dynamics by gene set differential coordination analysis.
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.
authors.affiliation: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA. tyu8@emory.edu
authors.forename: Tianwei
authors.initials: T
authors.lastname: Yu
authors.forename: Yun
authors.initials: Y
authors.lastname: Bai
mesh_terms: D003198:Computer Simulation; D030541:Databases, Genetic; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D055106:Genome-Wide Association Study / Q000379:methods; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D059467:Transcriptome