Statistical methods for ranking differentially expressed genes
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
A previous version of this manuscript was made available before peer review at http://genomebiology.com/2002/3/9/preprint/0007Published: 29 May 2003
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.