Bossers et al. BMC Genomics 2010, 11:112 http://www.biomedcentral.com/1471-2164/11/112 Page 7 of 13 Figure 7 Reproducibility of between-group treatment effects based on ratio and intensity models. Reproducibility of ANOVA-derived treatment effects between group 0 and group 6 in replicate brain datasets B1 and B2. Panel A: reproducibility of treatment effects derived from the ratio model. Panel B: reproducibility of treatment effects derived from the intensity model. Note the enhanced reproducibility when using the intensity-based ANOVA model. analysis resulted in better correlations between M-values from the two replicate datasets. A power perspective on intensity versus ratio-based models In both the cell line and human brain datasets, the fac- tors treatment and array are partially confounded (since one cannot assign every treatment to each array). In the MAQC dataset, which consists of 10 technical replica- tions of two commercially available RNA samples, these factors are not confounded. A BIC analysis revealed that the model without array effect is preferable for 71% of the genes in the MAQC dataset. Thus, even though dropping the array effect is beneficial for roughly 2 out of every 3 genes, this percentage is lower than in the other two data sets. This is however not unexpected: BIC includes a penalty which is proportional to the sam- ple size (number of arrays) for the model with array effect while constant for the other model, and the sam- ple size of the MAQC dataset is smaller than those of the other two data sets. The simple repeated measure- ments design of the MAQC data set allowed us to study the trade-off between less degrees of freedom and var- iance reduction caused by inclusion of the array effect from a power perspective. Figure 8A-C shows the power curves for probes A_32_P215304, A_23_P201338 and A_32_P211558, assuming that the estimated treatment effect sizes are real (the treatment effect sizes are the same for both the ratio and intensity models). While one analysis may dominate the other for all sample sizes (Figure 8A and 8B), we observe the aforementioned trade-off from the crossing curves in Figure 8C. In all cases the power curve for the analysis including the array effect is steeper, confirming our expectations that when sample size increases, the loss of degrees of free- dom is less harmful. We also considered the average power (the expected number of genes declared signifi- cant) over the entire MAQC dataset (Figure 8D). The gene set was restricted to those with an estimated treat- ment effect size larger than 0.25 to emulate a set that contains differential signal. The average power was higher for the model with array effect for total sample size larger or equal to 12 while smaller otherwise. Dif- ferences were small, though: a maximum difference of 1% was found. As expected the power curves converge again when the sample size increases. As opposed to the reproducibility results, these power calculations assumed the array effects to be fixed, as implied by the model. While this may be too optimistic, it is good to notice that the aforementioned trade-off is visible from a power perspective. Discussion Our results demonstrate that the analysis of dual-color microarray gene expression experiments using intensity- based linear models outperforms the standard ratio- based analysis. Both reproducibility and sensitivity were enhanced in detecting differential gene expression in two independent datasets. By analyzing technically replicated experiments we determined the effect of both models on the reproduci- bility of gene rankings. Our studies show that for both the cell line and brain datasets the intensity-based analy- sis provides more reproducible gene rankings than the ratio-based analysis of the same dataset. For the cell line dataset, 78% of the 1,000 most significant genes is