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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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Gene Filtering in the Analysis of Illumina Microarray Experiments

Anyiawung Chiara Forcheh1 / Geert Verbeke2 / Adetayo Kasim3 / Dan Lin4 / Ziv Shkedy5 / Willem Talloen6 / Hinrich WH Göhlmann7 / Lieven Clement8

1Katholieke Universiteit Leuven and Universiteit Hasselt

2Katholieke Universiteit Leuven and Universiteit Hasselt

3Durham University

4Katholieke Universiteit Leuven and Universiteit Hasselt

5Katholieke Universiteit Leuven and Universiteit Hasselt

6Janssen Pharmaceutica N. V.

7Johnson & Johnson Pharmaceutical Research & Development

8Katholieke Universiteit Leuven and Universiteit Hasselt

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 11, Issue 2, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1710, January 2012

Publication History

Published Online:

Illumina bead arrays are microarrays that contain a random number of technical replicates (beads) for every probe (bead type) within the same array. Typically around 30 beads are placed at random positions on the array surface, which opens unique opportunities for quality control. Most preprocessing methods for Illumina bead arrays are ported from the Affymetrix microarray platform and ignore the availability of the technical replicates. The large number of beads for a particular bead type on the same array, however, should be highly correlated, otherwise they just measure noise and can be removed from the downstream analysis. Hence, filtering bead types can be considered as an important step of the preprocessing procedure for Illumina platform. This paper proposes a filtering method for Illumina bead arrays, which builds upon the mixed model framework. Bead types are called informative/non-informative (I/NI) based on a trade-off between within and between array variabilities. The method is illustrated on a publicly available Illumina Spike-in data set (Dunning et al., 2008) and we also show that filtering results in a more powerful analysis of differentially expressed genes.

Keywords: illumina bead arrays; gene filtering; linear mixed model

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