Down-weighting overlapping genes improves gene set analysis

BMC Bioinformatics

Research Article

Sorin Draghici¹  Gaurav Bhatti²  Roberto Romero²  Adi Laurentiu Tarca³ 

[1] Department of Computer Science, Wayne State University, Detroit, MI, USA;Department of Clinical and Translational Science, Wayne State University, Detroit, MI, USA;Perinatology Research Branch, NICHD/NIH/DHHS, Bethesda, Detroit, Maryland, MI, USA;Perinatology Research Branch, NICHD/NIH/DHHS, Bethesda, Detroit, Maryland, MI, USA;Department of Computer Science, Wayne State University, Detroit, MI, USA;Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA;

关键词: Gene expression;    Gene set analysis;    Pathway analysis;    Overlapping gene sets;   

DOI  :  10.1186/1471-2105-13-136

 received in 2012-02-14, accepted in 2012-05-18,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundThe identification of gene sets that are significantly impacted in a given condition based on microarray data is a crucial step in current life science research. Most gene set analysis methods treat genes equally, regardless how specific they are to a given gene set.ResultsIn this work we propose a new gene set analysis method that computes a gene set score as the mean of absolute values of weighted moderated gene t-scores. The gene weights are designed to emphasize the genes appearing in few gene sets, versus genes that appear in many gene sets. We demonstrate the usefulness of the method when analyzing gene sets that correspond to the KEGG pathways, and hence we called our method PathwayAnalysis withDown-weighting ofOverlappingGenes (PADOG). Unlike most gene set analysis methods which are validated through the analysis of 2-3 data sets followed by a human interpretation of the results, the validation employed here uses 24 different data sets and a completely objective assessment scheme that makes minimal assumptions and eliminates the need for possibly biased human assessments of the analysis results.ConclusionsPADOG significantly improves gene set ranking and boosts sensitivity of analysis using information already available in the gene expression profiles and the collection of gene sets to be analyzed. The advantages of PADOG over other existing approaches are shown to be stable to changes in the database of gene sets to be analyzed. PADOG was implemented as an R package available at: http://bioinformaticsprb.med.wayne.edu/PADOG/or http://www.bioconductor.org.

【 授权许可】

CC BY   
© Tarca et al.; licensee BioMed Central Ltd. 2012