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Open Access Research article

Evaluation of time profile reconstruction from complex two-color microarray designs

Ana C Fierro123, Raphael Thuret12, Kristof Engelen4, Gilles Bernot3, Kathleen Marchal4* and Nicolas Pollet123

Author Affiliations

1 CNRS UMR 8080, Laboratoire Développement et Evolution, Bat 445, F-91405 Orsay, France

2 Univ Paris Sud, F-91405 Orsay, France

3 Programme d'Epigenomique – Genopole, Univ Evry, Tour Evry-2, Place des terrasses, 91000 Evry, France

4 Dep Microbial and Molecular Sciences, K.U.Leuven, Kasteelpark Arenberg 20, 3001 Leuven, Belgium

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BMC Bioinformatics 2008, 9:1  doi:10.1186/1471-2105-9-1

Published: 3 January 2008



As an alternative to the frequently used "reference design" for two-channel microarrays, other designs have been proposed. These designs have been shown to be more profitable from a theoretical point of view (more replicates of the conditions of interest for the same number of arrays). However, the interpretation of the measurements is less straightforward and a reconstruction method is needed to convert the observed ratios into the genuine profile of interest (e.g. a time profile). The potential advantages of using these alternative designs thus largely depend on the success of the profile reconstruction. Therefore, we compared to what extent different linear models agree with each other in reconstructing expression ratios and corresponding time profiles from a complex design.


On average the correlation between the estimated ratios was high, and all methods agreed with each other in predicting the same profile, especially for genes of which the expression profile showed a large variance across the different time points. Assessing the similarity in profile shape, it appears that, the more similar the underlying principles of the methods (model and input data), the more similar their results. Methods with a dye effect seemed more robust against array failure. The influence of a different normalization was not drastic and independent of the method used.


Including a dye effect such as in the methods lmbr_dye, anovaFix and anovaMix compensates for residual dye related inconsistencies in the data and renders the results more robust against array failure. Including random effects requires more parameters to be estimated and is only advised when a design is used with a sufficient number of replicates. Because of this, we believe lmbr_dye, anovaFix and anovaMix are most appropriate for practical use.