Title
Differential regulation enrichment analysis via the integration of transcriptional regulatory network and gene expression data.
Abstract
Motivation: Although many gene set analysis methods have been proposed to explore associations between a phenotype and a group of genes sharing common biological functions or involved in the same biological process, the underlying biological mechanisms of identified gene sets are typically unexplained. Results: We propose a method called Differential Regulation-based enrichment Analysis for GENe sets (DRAGEN) to identify gene sets in which a significant proportion of genes have their transcriptional regulatory patterns changed in a perturbed phenotype. We conduct comprehensive simulation studies to demonstrate the capability of our method in identifying differentially regulated gene sets. We further apply our method to three human microarray expression datasets, two with hormone treated and control samples and one concerning different cell cycle phases. Results indicate that the capability of DRAGEN in identifying phenotype-associated gene sets is significantly superior to those of four existing methods for analyzing differentially expressed gene sets. We conclude that the proposed differential regulation enrichment analysis method, though exploratory in nature, complements the existing gene set analysis methods and provides a promising new direction for the interpretation of gene expression data. Availability and implementation: The program of DRAGEN is freely available at http://bioinfo.au.tsinghua.edu.cn/dragen/. Contact: ruijiang@tsinghua.edu.cn or jiang@cs.ucr.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Year
DOI
Venue
2015
10.1093/bioinformatics/btu672
BIOINFORMATICS
Field
DocType
Volume
Biological process,Microarray,Gene,Phenotype,Computer science,Gene expression,Regulation of gene expression,Mechanism (biology),Computational biology,Cell cycle,Bioinformatics
Journal
31
Issue
ISSN
Citations 
4
1367-4803
2
PageRank 
References 
Authors
0.38
14
3
Name
Order
Citations
PageRank
Shining Ma150.80
Tao Jiang21809155.32
Rui Jiang333040.72