Bossers et al. BMC Genomics 2010, 11:112 http://www.biomedcentral.com/1471-2164/11/112 Analysis of variance (ANOVA) is a widely used tool to analyze and rank genes in both one- and dual-color comparative gene expression experiments [5]. The ANOVA model incorporates factors such as treatment, tissue and age to estimate the effect of interest per gene. For dual-color arrays specifically, an array effect is included in the model to determine the technical noise introduced by any between-array differences. Account- ing for such an array effect in the analysis of dual-color arrays was initially necessary due to the relatively poor quality of array platforms: researchers were confronted with different levels of background signals across arrays, and the process of spotting cDNAs yielded probes with different shapes and probe concentrations. The latest generations of commercial dual-color platforms however use synthesized oligonucleotide probes instead of cDNA probes, and are of much higher and consistent quality and concentration. Subsequently, the variance intro- duced by the array effect has become much less pro- nounced [6]. We recently reported that using the Agilent arrayCGH platform or other CGH array plat- forms, the separate channels of these dual channel arrays are interchangeable, avoiding redundant hybridi- zations of the same reference material in every experi- ment [7]. We therefore raised the question whether the results obtained by separately analyzing intensities from co-hybridized gene expression array samples are more reproducible than results based on classical ratio-based analysis. As an added benefit, an intensity-based analysis approach allows for pairwise comparison between any samples. We have performed a set of experiments to determine whether the intensity-based analysis of dual-color arrays is more reproducible than the conventional ratio-based analysis. Two independent datasets were used: a human keratinocyte cell line dataset, and a dataset based on human brain tissue. By selecting these datasets, we were able to study the performance of the ratio- and inten- sity-based models in two distinct situations: no biologi- cal variation (cell line dataset) versus substantial biological variation (brain dataset). For both the intensity-based and ratio-based analysis, we estimated the reproducibility and sensitivity in the detection of differential gene expression by analyzing technical replicates. Technical replicates, consisting of two non-overlapping sets of microarrays, were used rather than biological replicates, as our focus is on inclusion or exclusion of the array factor, which is a technical factor. Furthermore, we used a model selection algorithm to determine whether either intensity or ratio based analysis was most suitable for each dataset. Our results indicate that intensity-based analysis out- performs the standard ratio-based analysis of the same dataset. Intensity-based results are more reproducible, Page 2 of 13 and increase the sensitivity of detecting regulated genes. Our results also indicate that differences between ratio- and intensity-based results become smaller in large data- sets with simple designs, suggesting that more complex designs such as factorial and loop designs benefit most from our approach. Results Intensity-based analysis yields comparable results to ratio-based analysis, but is more sensitive in detecting differential gene expression To determine the feasibility of intensity-based analysis of dual-color arrays, we performed a microarray experi- ment in which the effects of 4 different treatments (T1, T2, T3 and T4) were investigated using two keratino- cyte-derived cell lines by measuring transcript levels on Agilent 4 × 44K Whole Human Genome arrays. The entire experiment was technically replicated (experiment C1 and C2, the hybridization setup can be found in Additional file 1). One of the prerequisites of intensity-based analysis of dual-color arrays is that co-hybridized samples do not influence gene expression measurements in the opposite channels. In other words, the intensity distribution of sample X should be independent of the co-hybridized sample. A hierarchical cluster analysis of the individual intensities of all arrays showed that cell line-treatment combinations invariably clustered together (Figure 1). This indeed suggests that intensities do not seem to be influenced by the co-hybridized sample or array used, as the array effect appears to be smaller than the treatment effect. To further investigate the magnitude of array-spe- cific effects we compared the relative effect sizes of the array and treatment effects as defined by the ANOVA model. This analysis revealed that, apart from the noise component introduced by genes that are not differen- tially expressed between treatments, the treatment effect is much larger than the array effect (Figure 2A). Conse- quently, we expected the in silico reconstructed ratios between two samples that were not co-hybridized, to be very similar to the directly measured ratios between those samples. We indeed observed a strong linear cor- relation between the directly measured ratios, and the in silico reconstructed ratios based on separate hybridiza- tions (average correlation 0.78, range 0.47-0.88, Addi- tional file 2). To investigate whether these findings translate to other datasets as well, we analyzed a publicly available dataset in which two commercially available samples were hybridized 10 times on Agilent dual-color microarrays (data obtained from the MAQC dataset [6]). We selected this experiment specifically, because treatment and array effects are not partially confounded (as is the case in the cell line dataset), and biological variance is