Bossers et al. BMC Genomics 2010, 11:112 http://www.biomedcentral.com/1471-2164/11/112 ratios, a phenomenon we observed in the brain dataset (Figure 7 and Additional file 5). The intensity-based ana- lysis of brain datasets B1 and B2 does not suffer from these drawbacks: no ratios are calculated, and more degrees of freedom are left for estimating the biological effect of interest, resulting in a substantial proportion of reproducible findings (51% of the 1,000 most significant genes), and a relatively high correlation between p-values (r = 0.46). In a setting with many biological replicates per level (e.g. comparison of two large groups) the differences in correlation between the ratio-based and intensity- based analysis are likely to be smaller. Our studies indicate that the reliability of gene rank- ings obtained from dual-color microarray experiments can be improved by using intensity-based models. An added benefit of the intensity-based analysis is that intensity models do not suffer from the drawbacks of ratio models in the analysis of complex direct dual-color experiments. Designs such as the interwoven loop design address the increased complexity of microarray experiments, which have progressed from “simple” two- group comparisons to multifactorial or time-course experiments. The aforementioned direct designs are effi- cient, but often bias certain comparisons over others and lack the possibility to extend the experiment by adding more groups or samples. There are no such lim- itations when analyzing dual-color experiments with intensity-based models [10]. Finally, the LIMMA soft- ware package also uses intensity data from dual-color experiments, but mainly as a solution to compare sam- ples which are unconnected in the hybridization design [11]. Here, we provide evidence that it is beneficial to perform an intensity-based analysis for connected designs as well. It should be noted that the observed improvements may be limited to dual-color arrays and that further experiments are needed to justify the gener- ality of these results for other array designs. Conclusions In conclusion, our results indicate that intensity-based models are very powerful in the analysis of dual-color gene expression data when these are obtained from a high-quality platform. Most importantly, intensity mod- els yield more reproducible results, and are more sensi- tive in the detection of differential gene expression than standard ratio-based analysis methods on the same microarray dataset. The gain in reproducibility and sen- sitivity are most pronounced in complex designs such as the interwoven loop design. We argue that the intensity- based models outperform ratio-based models, and thus are the preferred models for the analysis of dual-color gene expression datasets derived from commercial oligo-based array platforms. Page 10 of 13 Methods Human keratinocyte cell line dataset The cell line sample set consisted of two immortalized cell lines (cell lines 10 and 19) derived from a single pri- mary keratinocyte culture. The two cell lines were sub- jected to four different treatments (treatments T1, T2, T3 and T4). After RNA isolation and labeling (labeling performed with Agilent Low RNA Input Fluorescent Linear Amplification Kit, Agilent Technologies), equal amounts (1 μg) of Cy3-CTP and Cy5-CTP labeled sam- ples were hybridized to Agilent 4 × 44K Whole Human Genome arrays (Agilent Technologies, Part Number G4112F), according to the manufacturer’s instructions. The hybridization set-up on the 4 × 44K array was cho- sen in such a way that for each cell line, both Cy3- and Cy5-labeled samples for all treatments were hybridized on a single slide (containing 4 arrays). The entire experi- ment was technically replicated. The hybridization setup can be found in Additional file 1. Microarrays were scanned using the Agilent DNA Microarray Scanner (Agilent Technologies, Part Number G2505B), and scans were quantified using the Agilent Feature Extraction software (version 8.5.1). Raw expression data generated by the Feature Extraction software were imported into the R statistical environment using the LIMMA package [12] in Bioconductor http://www.bioconductor.org. No background correction was performed, as overall back- ground levels were very low. The intensity distributions within and between arrays were normalized using the quantile scaling algorithm [13] in LIMMA. After nor- malization, the separate intensity channels were extracted from the ratio measurements. The log2-trans- formed intensity measurements were used in all follow- ing analyses. The microarray data have been deposited in the Gene Express Omnibus (GEO) database http:// www.ncbi.nlm.nih.gov/geo/query/acc.cgi? token=djkxjoomeyecqja&acc=GSE12553. MAQC dataset Microarray hybridization data were extracted from the Gene Expression Omnibus (GEO accession number GSE5350, file MAQC_AGL_123_60TXTs.zip, series “C": arrays AGL_3_C1.txt, AGL_3_C2.txt, AGL_3_C3.txt, AGL_3_C4.txt and AGL_3_C5.txt, series “D": AGL_3_D1. txt, AGL_3_D2.txt, AGL_3_D3.txt, AGL_3_D4.txt and AGL_3_D5.txt). This dataset consists of 10 technical repli- cations of a hybridization of Stratagene Universal Human Reference RNA (Cy3 in series “C”, Cy5 in series “D”) and Ambion Human Brain Reference RNA (Cy3 in series “D”, Cy5 in series “C”) as described in [6]. Microarray normali- zation procedures were performed as described for the cell line experiment. Power curves were computed from the non-central t-distribution.