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Open Access Research article

Down-weighting overlapping genes improves gene set analysis

Adi Laurentiu Tarca123*, Sorin Draghici24, Gaurav Bhatti1 and Roberto Romero1

Author Affiliations

1 Perinatology Research Branch, NICHD/NIH/DHHS, , Bethesda, Maryland, and Detroit, MI, USA

2 Department of Computer Science, Wayne State University, Detroit, MI, USA

3 Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, USA

4 Department of Clinical and Translational Science, Wayne State University, Detroit, MI, USA

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BMC Bioinformatics 2012, 13:136  doi:10.1186/1471-2105-13-136

Published: 19 June 2012

Abstract

Background

The 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.

Results

In 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 Pathway Analysis with Down-weighting of Overlapping Genes (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.

Conclusions

PADOG 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/ webciteor http://www.bioconductor.org webcite.

Keywords:
Gene expression; Gene set analysis; Pathway analysis; Overlapping gene sets