Journal Article

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

Maureen A. Sartor, George D. Leikauf and Mario Medvedovic

in Bioinformatics

Volume 25, issue 2, pages 211-217
Published in print January 2009 | ISSN: 1367-4803
Published online November 2008 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btn592
LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data

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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@uc.edu

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

Journal Article.  5161 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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