Testing for differentially expressed genes with microarray data.
This paper compares the type I error and power of the one- and two-sample t-tests, and the one- and two-sample permutation tests for detecting differences in gene expression between two microarray samples with replicates using Monte Carlo simulations. When data are generated from a normal distribution, type I errors and powers of the one-sample parametric t-test and one-sample permutation test are very close, as are the two-sample t-test and two-sample permutation test, provided that the number of replicates is adequate. When data are generated from a t-distribution, the permutation tests outperform the corresponding parametric tests if the number of replicates is at least five. For data from a two-color dye swap experiment, the one-sample test appears to perform better than the two-sample test since expression measurements for control and treatment samples from the same spot are correlated. For data from independent samples, such as the one-channel array or two-channel array experiment using reference design, the two-sample t-tests appear more powerful than the one-sample t-tests.
Tsai
Chen-An
CA
Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
Chen
Yi-Ju
YJ
Chen
James J
JJ
D018076:DNA, Complementary / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D009010:Monte Carlo Method; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000592:standards*
D018076:DNA, Complementary