Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data

Background: Numerous feature selection methods have been applied to the identification of differentially expressed genes in microarray data. These include simple fold change, classical tstatistic and moderated t-statistics. Even though these methods return gene lists that are often dissimilar, few direct comparisons of these exist. We present an empirical study in which we compare some of the most commonly used feature selection methods. We apply these to 9 publicly available datasets, and compare, both the gene lists produced and how these perform in class prediction of test datasets. Results: In this study, we compared the efficiency of the feature selection methods; significance analysis of microarrays (SAM), analysis of variance (ANOVA), empirical bayes t-statistic, template matching, maxT, between group analysis (BGA), Area under the receiver operating characteristic (ROC) curve, the Welch t-statistic, fold change, rank products, and sets of randomly selected genes. In each case ...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Ian B. Jeffery, Desmond G. Higgins, Aedín C. Culhane
• Keywords: BMCBI 2006, Feature Selection Method, Fold Change, Gene List