The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data.
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.
Eichler
Gabriel S
GS
Genomics and Bioinformatics Groups, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.
Reimers
Mark
M
Kane
David
D
Weinstein
John N
JN
D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D045744:Cell Line, Tumor; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D005260:Female; D000077156:Gefitinib; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated; D011799:Quinazolines / Q000494:pharmacology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012907:Smoking
D011799:Quinazolines; D000077156:Gefitinib