Fischer Ernest A, Friedman Michael, Markey Mia K
Department of Biomedical Engineering,The University of Texas at Austin, Austin, TX, USA.
AMIA Annu Symp Proc. 2006;2006:921.
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.
