Ranking analysis of F-statistics for microarray data

Background: 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. Results: 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 twosample 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 different...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Yuan-De Tan, Myriam Fornage, Hongyan Xu
• Keywords: BMCBI 2008, False Discovery Rate, Microarray Data, Multiple Genes