Shining Ma1, Tao Jiang2, Rui Jiang2. 1. MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST; Department of Automation, Tsinghua University, Beijing 100084, China, Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA, Department of Statistics, Stanford University, Stanford, CA 94305, USA. 2. MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST; Department of Automation, Tsinghua University, Beijing 100084, China, Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA, Department of Statistics, Stanford University, Stanford, CA 94305, USA MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, TNLIST; Department of Automation, Tsinghua University, Beijing 100084, China, Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA, Department of Statistics, Stanford University, Stanford, CA 94305, USA.
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.
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.