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Bioinformatics Advance Access originally published online on July 28, 2006
Bioinformatics 2006 22(19):2364-2372; doi:10.1093/bioinformatics/btl402
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Partition resampling and extrapolation averaging: approximation methods for quantifying gene expression in large numbers of short oligonucleotide arrays

Darlene R. Goldstein

École Polytechnique Fédérale de Lausanne, EPFL Institut de mathématiques, Bâtiment MA, Station 8, CH-1015 Lausanne, Switzerland

Motivation: Studies of gene expression using high-density short oligonucleotide arrays have become a standard in a variety of biological contexts. Of the expression measures that have been proposed to quantify expression in these arrays, multi-chip-based measures have been shown to perform well. As gene expression studies increase in size, however, utilizing multi-chip expression measures is more challenging in terms of computing memory requirements and time.

Results: A strategic alternative to exact multi-chip quantification on a full large chip set is to approximate expression values based on subsets of chips. This paper introduces an extrapolation method, Extrapolation Averaging (EA), and a resampling method, Partition Resampling (PR), to approximate expression in large studies.

An examination of properties indicates that subset-based methods can perform well compared with exact expression quantification. The focus is on short oligonucleotide chips, but the same ideas apply equally well to any array type for which expression is quantified using an entire set of arrays, rather than for only a single array at a time.

Availability: Software implementing Partition Resampling and Extrapolation Averaging is under development as an R package for the BioConductor project.

Contact: Darlene.Goldstein{at}epfl.ch

Received on February 28, 2006; revised on June 13, 2006; accepted on July 18, 2006

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