On Selecting the Best Pre-processing Method for Affymetrix Genechips

Authors:
J. P. Florido;H. Pomares;I. Rojas;J. C. Calvo;J. M. Urquiza;M. Gonzalo Claros
Affiliations:
Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain;Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain;Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain;Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain;Department of Computer Architecture and Computer Technology, University of Granada, Granada, Spain;Department of Molecular Biology and Biochemistry, University of Málaga, Málaga, Spain
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

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Abstract

Affymetrix High Oligonucleotide expression arrays, also known as Affymetrix GeneChips, are widely used for the high-throughput assessment of gene expression of thousands of genes simultaneously. Although disputed by several authors, there are non-biological variations and systematic biases that must be removed as much as possible before an absolute expression level for every gene is assessed. Several pre-processing methods are available in the literature and five common ones (RMA, GCRMA, MAS5, dChip and VSN) and two customized Loess methods are benchmarked in terms of data variability, similarity of data distributions and correlation coefficient among replicated slides in a variety of real examples. Besides, it will be checked how the variant and invariant genes can influence on preprocessing performance.