Gene Filtering in the Analysis of Illumina Microarray Experiments

Forcheh Anyiawung Chiara, Verbeke Geert, Kasim Adetayo, Lin Dan, Shkedy Ziv, Talloen Willem, Göhlmann Hinrich Wh and Clement Lieven
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Forcheh Anyiawung Chiara: Katholieke Universiteit Leuven and Universiteit Hasselt
Verbeke Geert: Katholieke Universiteit Leuven and Universiteit Hasselt
Kasim Adetayo: Durham University
Lin Dan: Katholieke Universiteit Leuven and Universiteit Hasselt
Shkedy Ziv: Katholieke Universiteit Leuven and Universiteit Hasselt
Talloen Willem: Janssen Pharmaceutica N. V.
Göhlmann Hinrich Wh: Johnson & Johnson Pharmaceutical Research & Development
Clement Lieven: Katholieke Universiteit Leuven and Universiteit Hasselt

Statistical Applications in Genetics and Molecular Biology, 2012, vol. 11, issue 2, 19

Abstract: 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 (search for similar items in EconPapers)
Date: 2012
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DOI: 10.2202/1544-6115.1710

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