Summaries of Affymetrix GeneChip probe level data

Rafael Irizarry

Benjamin Bolstad

Francois Collin

Leslie Cope

Bridget Hobbs

Terence Speed

Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA, ^{Biostatistics Group, University of California, Berkeley, CA, USA,}^{Gene Logic Inc., Berkeley, CA, USA,}^{Mathematical Sciences Department, Johns Hopkins University, Baltimore, MD, USA,}^{Division of Genetics and Bioinformatics, WEHI, Melbourne, Australia and}^{Department of Statistics, University of California, Berkeley, CA, USA}

^{To whom correspondence should be addressed. Tel: +1 410 614 5157; Fax: +1 410 955 0958; Email:}ude.uhj@afar

Received 2002 Oct 8; Revised 2002 Nov 25; Accepted 2002 Nov 25.

Abstract

High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11–20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated.

Abstract

ACKNOWLEDGEMENTS

We would like to thank GeneLogic and Affymetrix for the data, in particular, Uwe Scherf, Yasmin D. Beazer-Barclay and Kristen J. Antonellis (GeneLogic). We would also like to thank the R core, Bioconductor and Laurent Gautier (Technical University of Denmark) for writing great code and Ron Brookmeyer, Thomas Cappola, Sabra Klein, Scott Zeger (Johns Hopkins University), Ken Simpson, Sam Wormald (Walter and Eliza Hall Institute), Cheng Li (Harvard University) and Earl Hubbell (Affymetrix) for their insightful comments. The work of R.I. is supported by the PGA U01 HL66583.

ACKNOWLEDGEMENTS

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