Methods for Identifying Differentially Expressed Genes: An Empirical Comparison

Microarray technology, which observes thousands of gene expressions at once, is one of the popular topics in recent decades. When it comes to the analysis of microarraydata to identify differentially expressed (DE) genes, many methods have been proposed and modified for improvement. 

However, the most popular methods such as Significance Analysis of Microarrays (SAM), samroc, fold change, and rank product are far from perfect. In order to determine which method is most powerful, it comes down to the characteristics of the sample and distribution of the gene expressions. The most practiced method is usually SAM or samroc butwhen the data tends to be skewed, the power of these methods decreases. With the concept that the median becomes a better measure of central tendency than the mean when the data is skewed, the test statistics of the SAM and fold change methods are modified in this paper. This study shows that the median modified fold change method improves the power for many cases when identifying DE genes if the data follows a lognormal distribution.