Advanced search
1 file | 528.74 KB Add to list
Author
Organization
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
MODEL, linear mixed model, gene filtering, illumina bead arrays

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 528.74 KB

Citation

Please use this url to cite or link to this publication:

MLA
Forcheh, Anyiawung Chiara, Geert Verbeke, Adetayo Kasim, et al. “Gene Filtering in the Analysis of Illumina Microarray Experiments.” STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY 11.2 (2012): n. pag. Print.
APA
Forcheh, A. C., Verbeke, G., Kasim, A., Lin, D., Shkedy, Z., Talloen, W., Goehlmann, H. W., et al. (2012). Gene filtering in the analysis of illumina microarray experiments. STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, 11(2).
Chicago author-date
Forcheh, Anyiawung Chiara, Geert Verbeke, Adetayo Kasim, Dan Lin, Ziv Shkedy, Willem Talloen, Hinrich WH Goehlmann, and Lieven Clement. 2012. “Gene Filtering in the Analysis of Illumina Microarray Experiments.” Statistical Applications in Genetics and Molecular Biology 11 (2).
Chicago author-date (all authors)
Forcheh, Anyiawung Chiara, Geert Verbeke, Adetayo Kasim, Dan Lin, Ziv Shkedy, Willem Talloen, Hinrich WH Goehlmann, and Lieven Clement. 2012. “Gene Filtering in the Analysis of Illumina Microarray Experiments.” Statistical Applications in Genetics and Molecular Biology 11 (2).
Vancouver
1.
Forcheh AC, Verbeke G, Kasim A, Lin D, Shkedy Z, Talloen W, et al. Gene filtering in the analysis of illumina microarray experiments. STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY. 2012;11(2).
IEEE
[1]
A. C. Forcheh et al., “Gene filtering in the analysis of illumina microarray experiments,” STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, vol. 11, no. 2, 2012.
@article{3003050,
  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.},
  articleno    = {3},
  author       = {Forcheh, Anyiawung Chiara and Verbeke, Geert and Kasim, Adetayo and Lin, Dan and Shkedy, Ziv and Talloen, Willem and Goehlmann, Hinrich WH and Clement, Lieven},
  issn         = {1544-6115},
  journal      = {STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY},
  keywords     = {MODEL,linear mixed model,gene filtering,illumina bead arrays},
  language     = {eng},
  number       = {2},
  pages        = {19},
  title        = {Gene filtering in the analysis of illumina microarray experiments},
  url          = {http://dx.doi.org/10.2202/1544-6115.1710},
  volume       = {11},
  year         = {2012},
}

Altmetric
View in Altmetric
Web of Science
Times cited: