Bossers et al. BMC Genomics 2010, 11:112 http://www.biomedcentral.com/1471-2164/11/112 design and the sample size (the number of arrays). For the relatively simple MAQC data set BIC selects the model with array effect for 29% of the genes, much more frequently than for both the brain and cell line data sets. The beneficial effect of dropping the array effect from the model seems more pronounced in experiments that employ direct designs to address com- plex comparisons, such as time series and multifactorial experiments. Adding to the enhanced reproducibility, intensity- based analysis is more sensitive in the detection of dif- ferential gene expression, as derived from more signifi- cant p-values. It is important to note that, by selecting the ratio-based p-value of the 1000th most significant gene as a cutoff, almost all of the 1000 genes (89% for dataset C1, 92% for dataset C2) are also significant in the intensity-based analysis using the same cutoff. Inter- estingly, this analysis also reveals that 3335 genes, not selected by the ratio model, are reproducibly more sig- nificant than the 1000th gene in the ratio results. This provides additional evidence for the enhanced sensitivity of the intensity model over the ratio model. Due to the poor reproducibility of the ratio-based results in the brain dataset, such calculations were not meaningful for that dataset. Enhanced sensitivity due to ignoring the array effect in the linear model The observation that ratio-derived p-values can be improved by intensity-based models can be attributed to the inclusion of the array effect in the ratio-based linear model. Pairing of data is a powerful concept for remov- ing subject specific bias. In particular, when the quality of the spot printing procedure is not constant (often the case with in-house spotted arrays), it is essential to account for an array effect in the ANOVA model [9]. But there is a price to pay: degrees of freedom [10]. The total number of degrees of freedom equals the number of samples. The array effect consumes almost half of the degrees of freedom. However, due to the high quality of commercially available dual-color oligonucleotide micro- arrays, we and others observed that the ratios of the same sample pair, measured on different arrays, are strongly correlated [6], which means that the array effect is likely to be very small. When using a ratio-based model to analyze the data, many degrees of freedom are used to estimate the array effect, explaining only a small proportion of the variability. This ultimately results in less significant p-values, a lower correlation between p- values from the two replicate experiments, and a smaller proportion of reproduced top-ranked genes. Indeed, the results from the model selection experiments clearly indicate that the model without array effect is the pre- ferred model for both datasets. It should be noted that Page 9 of 13 we do not state that the array effect is absent: our ana- lyses in fact show that an array effect is present in mod- ern dual color microarray experiment. Furthermore, the results from the power calculations for the MAQC data- set show that including the array effect can be slightly beneficial for certain sample sizes. However, we con- clude from our experiments that for both the brain and cell line datasets, the array effect is too small in compar- ison to the main factor of interest (treatment) to justify incorporation into the ANOVA model. A possible argument for the inclusion of the array effect is the potential competition for spot binding between the co-hybridized samples. However, our and other studies suggest that competition is not an issue [7,10]. This can be derived from the strong correlation between the real and in silico reconstructed ratios (see Additional files 2 and 3), and the hierarchical clustering in Figures 1 and 5. Our study was however not con- ducted to demonstrate that ratios can be reconstructed in silico by using separate intensities. Indeed, this has been demonstrated before [10]. Our specific aim was to compare the performance of ratio- and intensity-based methods based on the main outcome of comparative gene expression experiments: a list of ranked genes. As this gene ranking provides the basis for further research, it needs to be robust and reproducible. We show here that intensity-based methods provide more reproducible results and is more sensitive in detecting differential gene expression, and thus outperform the standard ratio-based analysis. Biological variation negatively affects ratio-based, but not intensity-based, replication As indicated earlier, in the human brain experiment, we observed a striking lack of reproducibility (r = 0.05) between p-values generated by the ratio model on the replicate datasets B1 and B2, whereas the intensity-based p-values reproduced quite well (r = 0.46). These findings can be attributed to the following. First of all, the overall p-values (both intensity- and ratio-based) are less signifi- cant in the human brain experiment than in the cell line experiment, due to the large biological variation between individuals. Second, due to the relatively low level of bio- logical replication, few degrees of freedom are left for estimating the biological effect. Third, the brain experi- ment was not designed with splitting the data into two technical replicates in mind. While the two data sets are biologically identical, the samples are paired differently on the arrays between the two replicate datasets (see Additional file 4). Since this pairing is more or less arbi- trary, the results should be robust against this artifact, but this is not necessarily the case for the ratio-based analysis. When the biological variation is large, different sample pairings may result in differences in measured