LRpath: A logistic regression approach for identifying enriched biological groups in gene expression data

Maureen A Sartor^1,2, George D. Leikauf³ and Mario Medvedovic^1,2,*

¹Department of Environmental Health, University of Cincinnati, Cincinnati, OH, USA.

²Center for Environmental Genetics, University of Cincinnati, Cincinnati, OH, USA.

³Department Environmental and Occupational Health, University of Pittsburgh, Pittsburgh, PA, USA.

 

Sartor MA, Leikauf GD, Medvedovic M: LRpath: A logistic regression approach for identifying enriched biological groups in gene expression data. Bioinformatics, 15;25(2): 211-7. 2009. (preprint)

Abstract

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 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 to 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 to 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@uc.edu

Supplementary information: Supplementary data are available at Bioinformatics online and at http://eh3.uc.edu/lrpath.

NEW!  LRpath function has been incorporated within the R package CLEAN. The function has been updated to use the new Bioconductor formats for functional annotations as well as to use other built-in and external functional categories accessible through CLEAN.  

The legacy LRpath function used in the original analysis can be downloaded here.