Bossers et al. BMC Genomics 2010, 11:112 http://www.biomedcentral.com/1471-2164/11/112 M E T H O D O L O G Y A R T I C L E Open Access Intensity-based analysis of dual-color gene expression data as an alternative to ratio-based analysis to enhance reproducibility Koen Bossers1*, Bauke Ylstra2, Ruud H Brakenhoff3, Serge J Smeets2, Joost Verhaagen1, Mark A van de Wiel2,4,5 Abstract Background: Ratio-based analysis is the current standard for the analysis of dual-color microarray data. Indeed, this method provides a powerful means to account for potential technical variations such as differences in background signal, spot size and spot concentration. However, current high density dual-color array platforms are of very high quality, and inter-array variance has become much less pronounced. We therefore raised the question whether it is feasible to use an intensity-based analysis rather than ratio-based analysis of dual-color microarray datasets. Furthermore, we compared performance of both ratio- and intensity-based analyses in terms of reproducibility and sensitivity for differential gene expression. Results: By analyzing three distinct and technically replicated datasets with either ratio- or intensity-based models, we determined that, when applied to the same dataset, intensity-based analysis of dual-color gene expression experiments yields 1) more reproducible results, and 2) is more sensitive in the detection of differentially expressed genes. These effects were most pronounced in experiments with large biological variation and complex hybridization designs. Furthermore, a power analysis revealed that for direct two-group comparisons above a certain sample size, ratio-based models have higher power, although the difference with intensity-based models is very small. Conclusions: Intensity-based analysis of dual-color datasets results in more reproducible results and increased sensitivity in the detection of differential gene expression than the analysis of the same dataset with ratio-based analysis. Complex dual-color setups such as interwoven loop designs benefit most from ignoring the array factor. The applicability of our approach to array platforms other than dual-color needs to be further investigated. Background During the last decade, microarray technology has evolved into an indispensable tool for high-throughput gene expression studies. For example, microarrays are now routinely applied to identify differentially expressed genes between paired sample series, classify tumors in prognostic groups, and identify transcriptional altera- tions during development [1-3]. Two main types of commercial high density microarray platforms have emerged: one-color oligonucleotide platforms such as Affymetrix and Illumina, and dual-color oligonucleotide platforms such as Agilent and Nimblegen. Dual-color gene expression platforms are very efficient in directly * Correspondence: k.bossers@nin.knaw.nl 1Neuroregeneration Laboratory, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands comparing two conditions, by hybridizing the two con- ditions together on the same array. This greatly reduces the possible confounding effects of inter-array variability and local array effects. The outcome of comparative microarray experiments is a ranked list of significant genes, possibly involved in the process under investigation. The resulting gene list then serves as a starting point for further investigations, such as constructing new hypotheses, or the in vitro characterization of putatively identified genes. Due to their dimensionality (few observations, many variables), microarray experiments suffer from high rates of false positive and negative findings [4]. Thus, the issue of reproducibility is of utmost importance in array experiments. © 2010 Bossers et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.