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

Seo Young Kim; Jae Won Lee; In Suk Sohn

doi:10.1191/0962280206sm423oa

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

Seo Young Kim·Jae Won Lee·In Suk Sohn

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Abstract

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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Gene Expression Humans Korea Models, Statistical Oligonucleotide Array Sequence Analysis Comparative Study Journal Article Research Support, Non-U.S. Gov't

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Seo Young Kim, Jae Won Lee, In Suk Sohn. 2006. Comparison of various statistical methods for identifying differential gene expression in replicated microarray data.. https://doi.org/10.1191/0962280206sm423oa

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