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Biostatistics Advance Access published online on December 22, 2006
Biostatistics, doi:10.1093/biostatistics/kxl042
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Exploration, Normalization, and Genotype Calls of High Density Oligonucleotide SNP Array Data

Benilton Carvalho

Department of Biostatistics, Johns Hopkins University, Baltimore MD 21205

Henrik Bengtsson

Department of Statistics, UC Berkeley, CA

Terence P. Speed

Division of Genetics and Bioinformatics, WEHI, Melbourne, Australia
Department of Statistics, UC Berkeley, CA

Rafael A. Irizarry*

Department of Biostatistics, Johns Hopkins University, Baltimore MD 21205, rafa{at}jhu.edu

* To whom correspondence should be addressed

In most microarray technologies, a number of critical steps are required to convert raw intensity measurements into the data relied upon by data analysts, biologists and clinicians. These data manipulations, referred to as preprocessing, can influence the quality of the ultimate measurements. In the last few years, the high-throughput measurement of gene expression is the most popular application of microarray technology. For this application, various groups have demonstrated that the use of modern statistical methodology can substantially improve accuracy and precision of gene expression measurements, relative to ad-hoc procedures introduced by designers and manufacturers of the technology. Currently, other applications of microarrays are becoming more and more popular. In this paper we describe a preprocessing methodology for a technology designed for the identification of DNA sequence variants in specific genes or regions of the human genome that are associated with phenotypes of interest such as disease. In particular we describe methodology useful for preprocessing Affymetrix SNP chips and obtaining genotype calls with the preprocessed data. We demonstrate how our procedure improves existing approaches using data from three relatively large studies including one in which large numbers of independent calls are available. The proposed methods are implemented in the package oligo available from Bioconductor.


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