Power improvement of alpha-spending p-values with respect to the ordinary t-test

<p><b>Copyright information:</b></p><p>Taken from "An adaptive alpha spending algorithm improves the power of statistical inference in microarray data analysis"</p><p>Bioinformation 2007;1(10):384-389.</p><p>Published online 10 Apr 2007</p><p>PMCID:PMC1896052.</p><p></p> The results are from the partial null hypothesis simulations with 20% of the genes differentially expressed and correlated with the same correlation coefficient and 80% of the genes non-differentially expressed and uncorrelated. For = 700 , the 700 = 7x100 simulated data sets per plot were obtained by independently generating 100 data sets for each of seven different values of the population mean differential expression Δ . These seven values of Δ = Δ(1– ) were obtained such that the corresponding power of the ordinary t-test in detecting the differentially expressed genes was varied by1– = 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8 . For = 2000 the 30 simulated data sets correspond to 1– = 0.5 only. The situation = 2000 is simulated for = 4, 6 but not for =1