Bossers et al. BMC Genomics 2010, 11:112 http://www.biomedcentral.com/1471-2164/11/112 Page 3 of 13 Figure 1 Intensities of the same sample measured on separate arrays are highly correlated. Hierarchical clustering of log2-transformed single channel intensities of the complete cell line experiment. Only genes with an average intensity A > 7 were used. Note that identical cell line-treatment combinations always cluster together, regardless of the co-hybridized sample. Sample naming = [cell line] [treatment] [duplicate set]. Figure 2 Comparison of effect sizes for array and treatment factors. Comparison between the relative sizes of the array and treatment effects, derived from the ANOVA model. Panel A: cell line dataset. Panel B: MAQC dataset. Dashed line: smoothed histogram over all genes for treatment effect size (absolute value of M-value), averaged over all treatment comparisons. Solid line: smoothed histogram over all genes for average array effect size (absolute value of M-value). Note that both the treatment and array effects still include an unavoidable noise component, hence one expects a partial overlap in the histograms because of genes that do not show a differential effect between treatments. Still there is a clear proportion of genes for which the mean treatment effect is much larger than the array effect size. absent. We observed a very strong linear correlation between real and in silico reconstructed ratios (Addi- tional file 3), and the ANOVA-derived array effect was very small compared to the treatment effect (Figure 2B). Ultimately, the reliable detection of differential gene expression between treatments is the main interest of the cell line experiment. We therefore, for the cell line dataset, compared the ranking of genes by p-values gen- erated with two different ANOVA models: one includ- ing the array effect (the ratio analysis), and one without the array effect (the intensity analysis). We observed a substantial overlap of 64% (replicate dataset C1) and 66% (replicate dataset C2) between the ratio- and inten- sity-based lists of the 1,000 most significant genes, sug- gesting that the intensity-based model yields similar results as the ratio-based model (Figure 3). Furthermore, when using the p-value of the 1,000th most significant gene in the ratio dataset as a cutoff for the intensity dataset, 89% (dataset C1) and 92% (dataset C2) of the 1,000 genes selected by the ratio model were also pre- sent in the set of intensity-selected genes. Interestingly, the p-values generated by the intensity model are smal- ler than those generated by the ratio model, indicating that the intensity model is more sensitive in detecting differential gene expression. Intensity-based results reproduce better than ratio-based results The detection of regulated genes should be reproduci- ble; between two technically replicated experiments, one