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Statistical Applications in Genetics and Molecular Biology

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1544-6115
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Gene Filtering in the Analysis of Illumina Microarray Experiments

Anyiawung Chiara Forcheh
  • Katholieke Universiteit Leuven and Universiteit Hasselt
/ Geert Verbeke
  • Katholieke Universiteit Leuven and Universiteit Hasselt
/ Adetayo Kasim
  • Durham University
/ Dan Lin
  • Katholieke Universiteit Leuven and Universiteit Hasselt
/ Ziv Shkedy
  • Katholieke Universiteit Leuven and Universiteit Hasselt
/ Willem Talloen
  • Janssen Pharmaceutica N. V.
/ Hinrich WH Göhlmann
  • Johnson & Johnson Pharmaceutical Research & Development
/ Lieven Clement
  • Katholieke Universiteit Leuven and Universiteit Hasselt
Published Online: 2012-01-06 | DOI: https://doi.org/10.2202/1544-6115.1710

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


Published Online: 2012-01-06


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

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