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Bioinformatics Advance Access published online on July 28, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl399
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received December 23, 2005
Revised June 9, 2006
Accepted July 18, 2006

Article

Joint estimation of calibration and expression for high-density oligonucleotide arrays

Ann L. Oberg 1 *, Douglas W. Mahoney 1, Karla V. Ballman 1, and Terry M. Therneau 1

1 Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN 55905, USA

* To whom correspondence should be addressed.
Ann L. Oberg, E-mail: oberg.ann{at}mayo.edu


  Abstract

Motivation: The need for normalization in microarray experiments has been well documented in the literature. Currently, many analysis methods treat normalization and analysis as a series of steps, with summarized data carried forward to the next step.

Results: We present a unified algorithm which incorporates normalization and class comparison in one analysis using probe level perfect match and mismatch data. The algorithm is based on calibration models common to most biological assays, and the resulting chip-specific parameters have a natural interpretation. We show that the algorithm fits into the statistical generalized linear models framework, describe a practical fitting strategy and present results of the algorithm applied to an example data set as well as based on metrics used in affycomp [7, 12]. The algorithm ranks amongst the top third of the affycomp competitors, performing best in measures of bias.

Availability: R functions are available on request from the authors.

Associate Editor: Alvis Brazma
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