Open Access Methodology article

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 and Mark A van de Wiel245

Author Affiliations

1 Neuroregeneration Laboratory, Netherlands Institute for Neuroscience, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands

2 Microarray Core Facility, Department of Pathology, VU University Medical Center, PO Box 7075, 1007 MB Amsterdam, the Netherlands

3 Section of Tumour Biology, Department of Otolaryngology/Head-Neck Surgery, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, the Netherlands

4 Department of Mathematics, Vrije Universiteit, De Boelelaan 1081a, 1081 HV Amsterdam, the Netherlands

5 Department of Biostatistics, VU University Medical Center, PO Box 7075, 1007 MB Amsterdam, the Netherlands

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BMC Genomics 2010, 11:112  doi:10.1186/1471-2164-11-112

Published: 17 February 2010



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.


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.


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.