Empirical comparison of tests for differential expression on simulated time series microarray experiments.
Methods for identifying differential expression were compared on time series microarray data from artificial gene networks. Identifying differential expression was dependent on normalization and whether the background was removed. Loess after background correction improved results for most methods. On data without background correction median centering improved performance. We recommend Cui and Churchill's ANOVA variants on background subtracted data and Efron and Tibshirani's Empirical Bayes Wilcoxon Rank Sum test when the background cannot be removed.
Fischer
Ernest A
EA
Department of Biomedical Engineering,The University of Texas at Austin, Austin, TX, USA.
Friedman
Michael
M
Markey
Mia K
MK
D000704:Analysis of Variance; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D053263:Gene Regulatory Networks; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D012372:ROC Curve