Empirical comparison of tests for differential expression on time-series microarray experiments.
Methods for identifying differentially expressed genes were compared on time-series microarray data simulated from artificial gene networks. Select methods were further analyzed on existing immune response data of Boldrick et al. (2002, Proc. Natl. Acad. Sci. USA 99, 972-977). Based on the simulations, we recommend the ANOVA variants of Cui and Churchill. Efron and Tibshirani's empirical Bayes Wilcoxon rank sum test is recommended when the background cannot be effectively corrected. Our proposed GSVD-based differential expression method was shown to detect subtle changes. ANOVA combined with GSVD was consistent on background-normalized simulation data. GSVD with empirical Bayes was consistent without background correction. Based on the Boldrick et al. data, ANOVA is best suited to detect changes in temporal data, while GSVD and empirical Bayes effectively detect individual spikes or overall shifts, respectively. For methods tested on simulation data, lowess after background correction improved results. On simulation data without background correction, lowess decreased performance compared to median centering.
Fischer
Ernest A
EA
Department of Biomedical Engineering, University of Texas at Austin, Campus Code C0800, 1 University Station, Austin, TX 78712, USA.
Friedman
Michael A
MA
Markey
Mia K
MK
D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D008722:Methods; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D013995:Time