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Improved Statistical Testing of Two-class Microarrays with a Robust Statistical Approach

  • Oh, Hee-Seok (Department of Statistics, Seoul National University) ;
  • Jang, Dong-Ik (Department of Statistics, Seoul National University) ;
  • Oh, Seung-Yoon (Interdisciplinary Program in Bioinformatics, Seoul National University) ;
  • Kim, Hee-Bal (Interdisciplinary Program in Bioinformatics, Seoul National University)
  • Received : 2010.03.17
  • Accepted : 2010.05.31
  • Published : 2010.06.30

Abstract

The most common type of microarray experiment has a simple design using microarray data obtained from two different groups or conditions. A typical method to identify differentially expressed genes (DEGs) between two conditions is the conventional Student's t-test. The t-test is based on the simple estimation of the population variance for a gene using the sample variance of its expression levels. Although empirical Bayes approach improves on the t-statistic by not giving a high rank to genes only because they have a small sample variance, the basic assumption for this is same as the ordinary t-test which is the equality of variances across experimental groups. The t-test and empirical Bayes approach suffer from low statistical power because of the assumption of normal and unimodal distributions for the microarray data analysis. We propose a method to address these problems that is robust to outliers or skewed data, while maintaining the advantages of the classical t-test or modified t-statistics. The resulting data transformation to fit the normality assumption increases the statistical power for identifying DEGs using these statistics.

Acknowledgement

Supported by : Korea Research Foundation

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