Method

The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data

Gabriel S Eichler^1,2 , Mark Reimers^1,3 , David Kane^1,4 and John N Weinstein¹

¹Genomics and Bioinformatics Groups, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA

²Bioinformatics Program, Boston University, Cummington St, Boston, Massachusetts 02215, USA

³Virginia Commonwealth University, Biostatistics Department, E Marshall St, Richmond, Virginia 23284, USA

⁴SRA International, Fair Lakes Court, Fairfax, Virginia 22033, USA

author email corresponding author email

Genome Biology 2007, 8:R187doi:10.1186/gb-2007-8-9-r187

Published:	10 September 2007

Subject areas: Bioinformatics, Genome studies

Abstract

Interpretation of microarray data remains a challenge, and most methods fail to consider the complex, nonlinear regulation of gene expression. To address that limitation, we introduce Learner of Functional Enrichment (LeFE), a statistical/machine learning algorithm based on Random Forest, and demonstrate it on several diverse datasets: smoker/never smoker, breast cancer classification, and cancer drug sensitivity. We also compare it with previously published algorithms, including Gene Set Enrichment Analysis. LeFE regularly identifies statistically significant functional themes consistent with known biology.