Research article
Impact of the spotted microarray preprocessing method on fold-change compression and variance stability
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* Corresponding author: Jérôme Ambroise jerome.ambroise@uclouvain.be
1 ICTEAM institute, ELEN department, Université Catholique de Louvain, Place du Levant 2, 1348 Louvain-la-Neuve, Belgium
2 IREC institute, Center for Applied Molecular Technologies, Université Catholique de Louvain, Clos Chapelle-aux-Champs 30, 1200 Bruxelles, Belgium
3 IREC institute, Epidemiology and Biostatistics department, Université Catholique de Louvain, Clos Chapelle-aux-Champs 30, 1200 Bruxelles, Belgium
4 Institute of Statistics, Biotstatistics and Actuarial Science, Université Catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
BMC Bioinformatics 2011, 12:413 doi:10.1186/1471-2105-12-413
Published: 25 October 2011Abstract
Background
The standard approach for preprocessing spotted microarray data is to subtract the local background intensity from the spot foreground intensity, to perform a log2 transformation and to normalize the data with a global median or a lowess normalization. Although well motivated, standard approaches for background correction and for transformation have been widely criticized because they produce high variance at low intensities. Whereas various alternatives to the standard background correction methods and to log2 transformation were proposed, impacts of both successive preprocessing steps were not compared in an objective way.
Results
In this study, we assessed the impact of eight preprocessing methods combining four background correction methods and two transformations (the log2 and the glog), by using data from the MAQC study. The current results indicate that most preprocessing methods produce fold-change compression at low intensities. Fold-change compression was minimized using the Standard and the Edwards background correction methods coupled with a log2 transformation. The drawback of both methods is a high variance at low intensities which consequently produced poor estimations of the p-values. On the other hand, effective stabilization of the variance as well as better estimations of the p-values were observed after the glog transformation.
Conclusion
As both fold-change magnitudes and p-values are important in the context of microarray class comparison studies, we therefore recommend to combine the Edwards correction with a hybrid transformation method that uses the log2 transformation to estimate fold-change magnitudes and the glog transformation to estimate p-values.