Bioinformatics Advance Access originally published online on November 27, 2008
Bioinformatics 2009 25(2):211-217; doi:10.1093/bioinformatics/btn592
LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data
1Center for Computational Medicine and Biology, University of Michigan, Ann Arbor, MI, 2Department Environmental and Occupational Health, University of Pittsburgh, Pittsburgh, PA, 3Department of Environmental Health and 4Center for Environmental Genetics, University of Cincinnati, Cincinnati, OH, USA
*To whom correspondence should be addressed.
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Abstract |
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Motivation: The elucidation of biological pathways enriched with differentially expressed genes has become an integral part of the analysis and interpretation of microarray data. Several statistical methods are commonly used in this context, but the question of the optimal approach has still not been resolved.
Results: We present a logistic regression-based method (LRpath) for identifying predefined sets of biologically related genes enriched with (or depleted of) differentially expressed transcripts in microarray experiments. We functionally relate the odds of gene set membership with the significance of differential expression, and calculate adjusted P-values as a measure of statistical significance. The new approach is compared with Fisher's exact test and other relevant methods in a simulation study and in the analysis of two breast cancer datasets. Overall results were concordant between the simulation study and the experimental data analysis, and provide useful information to investigators seeking to choose the appropriate method. LRpath displayed robust behavior and improved statistical power compared with tested alternatives. It is applicable in experiments involving two or more sample types, and accepts significance statistics of the investigator's choice as input.
Availability: An R function implementing LRpath can be downloaded from http://eh3.uc.edu/lrpath.
Contact: mario.medvedovic{at}uc.edu
Supplementary information: Supplementary data are available at Bioinformatics online and at http://eh3.uc.edu/lrpath.
Received on June 6, 2008; revised on October 13, 2008; accepted on November 11, 2008
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