Mar 8, 2008

Ranking analysis of F-statistics for microarray data

BMC Bioinformatics
Yuan-De TanHongyan Xu

Abstract

Microarray technology provides an efficient means for globally exploring physiological processes governed by the coordinated expression of multiple genes. However, identification of genes differentially expressed in microarray experiments is challenging because of their potentially high type I error rate. Methods for large-scale statistical analyses have been developed but most of them are applicable to two-sample or two-condition data. We developed a large-scale multiple-group F-test based method, named ranking analysis of F-statistics (RAF), which is an extension of ranking analysis of microarray data (RAM) for two-sample t-test. In this method, we proposed a novel random splitting approach to generate the null distribution instead of using permutation, which may not be appropriate for microarray data. We also implemented a two-simulation strategy to estimate the false discovery rate. Simulation results suggested that it has higher efficiency in finding differentially expressed genes among multiple classes at a lower false discovery rate than some commonly used methods. By applying our method to the experimental data, we found 107 genes having significantly differential expressions among 4 treatments at <0.7% FDR, of which 31...Continue Reading

Mentioned in this Paper

Neuro-Oncological Ventral Antigen 2
In Silico
Neurotrophic Tyrosine Kinase Receptor Type 2
Fluctuation
Microarray Analysis
CPLX2 gene
EGI gene
Genes, Reiterated
Brain
NTRK2 gene

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