Bossers et al. BMC Genomics 2010, 11:112 http://www.biomedcentral.com/1471-2164/11/112 Page 4 of 13 Figure 3 Ratio- and intensity-based analysis results in similar sets of differentially expressed genes. For the cell line dataset C1, p-values generated by the ratio and intensity ANOVA models were ranked from low to high, and assigned to bins containing 1000 genes. The fraction of overlap represents the proportion of genes occurring in both sets. expects to find highly similar sets of differentially expressed genes. To assess the reproducibility of the ratio and intensity models, we separately calculated p- values for both technical replicate datasets C1 and C2 of the cell line experiment. We observed a strong correla- tion (r = 0.75) between p-values generated by the ratio model for the two datasets (Figure 4A). However, the correlation between the p-values generated by the inten- sity model was even more pronounced (r = 0.82, Figure 4B). Next, we compared, for increasing numbers of genes, the overlap between the highest ranked genes within the replicate datasets, based on p-values gener- ated either by the ratio- or intensity based ANOVA models (Figure 4C). Regardless of the size of the top- ranked gene lists (n = 10-1,000, increments of 10), the intensity model outperforms the ratio model: more genes are reproduced by the intensity model. The results from the p-value rank-based reproducibil- ity experiments suggest that it is preferable to exclude the array factor in the linear model. To provide further evidence that the intensity model is indeed superior to the ratio model, we applied the Bayesian information criterion (BIC) for model selection on the ratio- and intensity-based ANOVA models [8]. This test does not directly compare the outcomes of the intensity- and ratio-based analysis, but rather determines which of the two analyses is most suitable to analyze the data. A BIC calculation between the ratio and intensity models was performed for each gene. Indeed, for 94.5% of the genes, the intensity model is favorable over the ratio model, as determined by lower BIC values. For 5.5% of the genes, the inclusion of an array effect in the linear model resulted in lower BIC values (Table 1). Analysis of an independent dataset confirms the gain in reproducibility and sensitivity when using intensity-based models The cell line experiments demonstrated that intensity- based analysis of dual-color data provide more reprodu- cible results, and is more sensitive in the detection of differentially expressed genes. It is however unknown how these results translate to other types of experi- ments, consisting of different sample types and experi- mental setups. We therefore analyzed a separate dataset, consisting of 49 human prefrontal cortex samples, divided over 7 equally sized groups. Samples were obtained from different human subjects, thus the biolo- gical variance in this dataset is expected to be large (no biological variation is present in the cell line dataset). Consequently, samples were not pooled, but hybridized individually. As each biological sample was hybridized two times (Additional file 4), we expected the distribu- tion of intensities from the two separate hybridizations to be very similar. Indeed, as previously observed in the cell line experiments, an unsupervised hierarchical clus- tering of all single channels showed that the two inten- sity measurements of the same biological sample clustered together (Figure 5). The human brain experiment was not designed with full technical replication in mind. However, as we performed duplicate measurements for each sample, it was possible to divide the dataset into 2 identical biological datasets B1 and B2 (see Additional file 4). To assess the reprodu- cibility between these two replicate datasets, we again compared the p-values generated both by the ratio and the intensity models. We found a surprising lack of cor- relation (r = 0.05) for p-values based on the ratio model between replicates (Figure 6A). The p-values generated by the intensity model however showed a far better cor- relation (r = 0.46, Figure 6B). Indeed, when determining, for increasing numbers of genes (10-1,000 genes, incre- ments of 10), the overlap between the highest ranked genes based on the p-values of the replicate measure- ments of either the ratio- or intensity based ANOVA models, we observed a substantial proportion of repro- duced genes in the intensity model. Almost no genes were reproduced between the two ratio-based analyses (Figure 6C). Not surprisingly, model selection according to the BIC showed that for 99.99% of the genes, the intensity model outperforms the ratio model. For only 4 genes, the array component was large enough to justify incorporation in the ANOVA model (Table 1). Thus,