Open Access Open Badges Methodology article

Assessment and optimisation of normalisation methods for dual-colour antibody microarrays

Martin Sill1*, Christoph Schröder2, Jörg D Hoheisel2, Axel Benner1 and Manuela Zucknick1

Author Affiliations

1 Division of Biostatistics, German Cancer Research Center, Im Neuenheimer Feld 280, Heidelberg, Germany

2 Division of Functional Genome Analysis, German Cancer Research Center, Im Neuenheimer Feld 580, Heidelberg, Germany

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BMC Bioinformatics 2010, 11:556  doi:10.1186/1471-2105-11-556

Published: 12 November 2010



Recent advances in antibody microarray technology have made it possible to measure the expression of hundreds of proteins simultaneously in a competitive dual-colour approach similar to dual-colour gene expression microarrays. Thus, the established normalisation methods for gene expression microarrays, e.g. loess regression, can in principle be applied to protein microarrays. However, the typical assumptions of such normalisation methods might be violated due to a bias in the selection of the proteins to be measured. Due to high costs and limited availability of high quality antibodies, the current arrays usually focus on a high proportion of regulated targets. Housekeeping features could be used to circumvent this problem, but they are typically underrepresented on protein arrays. Therefore, it might be beneficial to select invariant features among the features already represented on available arrays for normalisation by a dedicated selection algorithm.


We compare the performance of several normalisation methods that have been established for dual-colour gene expression microarrays. The focus is on an invariant selection algorithm, for which effective improvements are proposed. In a simulation study the performances of the different normalisation methods are compared with respect to their impact on the ability to correctly detect differentially expressed features. Furthermore, we apply the different normalisation methods to a pancreatic cancer data set to assess the impact on the classification power.


The simulation study and the data application demonstrate the superior performance of the improved invariant selection algorithms in comparison to other normalisation methods, especially in situations where the assumptions of the usual global loess normalisation are violated.