A power law global error model for the identification of differentially expressed genes in microarray data

BMC Bioinformatics

Table 1 Effect of the presence of DEG when applying the classic permutation strategy to the PLGEM-STN statistic.

Eight data sets with different percentages of DEG were constructed from the Latin Square data set by keeping the 62 known spiked-in probe sets, but randomly removing increasing amounts of the remaining probe sets reaching the total indicated in the first column. The null distributions of the PLGEM-STN statistics were evaluated through the classic permutation strategy either including or excluding DEG. A wide range of significance levels was used to select DEG and correlation between the FPR and the significance level or between estimated and observed FDR was evaluated through linear regressions in log-log plots. Table reports slopes, intercepts and adjusted r² of linear models. See text for details on estimation of FDR.

ISSN: 1471-2105