Method
Statistical methods for ranking differentially expressed genes
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Correspondence: Per Broberg per.broberg@astrazeneca.com
Molecular Sciences, AstraZeneca Research and Development Lund, S-221 87 Lund, Sweden
Genome Biology 2003, 4:R41 doi:10.1186/gb-2003-4-6-r41
Published: 29 May 2003Abstract
In the analysis of microarray data the identification of differential expression is paramount. Here I outline a method for finding an optimal test statistic with which to rank genes with respect to differential expression. Tests of the method show that it allows generation of top gene lists that give few false positives and few false negatives. Estimation of the false-negative as well as the false-positive rate lies at the heart of the method.