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Comparison of various statistical methods for identifying differential gene expression in replicated microarray data

Research Institute for Basic Science, Chonnam National University, Gwangju, Korea

Jae Won Lee

Department of Statistics, Korea University, Seoul, Korea, jael{at}korea.ac.kr

In Suk Sohn

Department of Statistics, Korea University, Seoul, Korea

DNA microarray is a new tool in biotechnology, which allows the simultaneous monitoring of thousands of gene expression in cells. The goal of differential gene expression analysis is to identify those genes whose expression levels change significantly by the experimental conditions. Although various statistical methods have been suggested to confirm differential gene expression, only a few studies compared the performance of the statistical tests. In our study, we extensively compared three types of parametric methods such as T-test, B-statistic and Bayes T-test and three types of non-parametric methods such as samroc, significance analysis of microarray and a modified mixture model using both the simulated datasets and the three real microarray experiments.

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