Open Access
December 2007 A statistical framework for the analysis of microarray probe-level data
Zhijin Wu, Rafael A. Irizarry
Ann. Appl. Stat. 1(2): 333-357 (December 2007). DOI: 10.1214/07-AOAS116


In microarray technology, a number of critical steps are required to convert the raw measurements into the data relied upon by biologists and clinicians. These data manipulations, referred to as preprocessing, influence the quality of the ultimate measurements and studies that rely upon them. Standard operating procedure for microarray researchers is to use preprocessed data as the starting point for the statistical analyses that produce reported results. This has prevented many researchers from carefully considering their choice of preprocessing methodology. Furthermore, the fact that the preprocessing step affects the stochastic properties of the final statistical summaries is often ignored. In this paper we propose a statistical framework that permits the integration of preprocessing into the standard statistical analysis flow of microarray data. This general framework is relevant in many microarray platforms and motivates targeted analysis methods for specific applications. We demonstrate its usefulness by applying the idea in three different applications of the technology.


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Zhijin Wu. Rafael A. Irizarry. "A statistical framework for the analysis of microarray probe-level data." Ann. Appl. Stat. 1 (2) 333 - 357, December 2007.


Published: December 2007
First available in Project Euclid: 30 November 2007

zbMATH: 1126.62111
MathSciNet: MR2415738
Digital Object Identifier: 10.1214/07-AOAS116

Keywords: background noise , microarray , normalization , preprocessing , probe level models

Rights: Copyright © 2007 Institute of Mathematical Statistics

Vol.1 • No. 2 • December 2007
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