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Biostatistics Advance Access published online on December 12, 2006
Biostatistics, doi:10.1093/biostatistics/kxl041
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

When should one subtract background fluorescence in two color microarrays?

Robert B. Scharpf*

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

Christine A. Iacobuzio-Donahue

Departments of Pathology and Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Julie B. Sneddon

Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA

Giovanni Parmigiani

Departments of Biostatistics and Oncology, Johns Hopkins Bloomberg School of Public Health and Johns Hopkins University School of Medicine, Baltimore, MD, USA

* Corresponding author: rscharpf{at}jhsph.edu

Two color microarrays are a powerful tool for genomic analysis, but have noise components that make inferences regarding gene expression inefficient and potentially misleading. Background fluorescence, whether attributable to non-specific binding or other sources, is an important component of noise. The decision to subtract fluorescence surrounding spots of hybridization from spot fluorescence has been controversial, with no clear criteria for determining circumstances that may favor, or disfavor, background subtraction. While it is generally accepted that subtracting background reduces bias but increases variance in the estimates of the ratios of interest, no formal analysis of the bias-variance trade off of background subtraction has been undertaken. In this paper, we use simulation to systematically examine the bias-variance trade off under a variety of possible experimental conditions. Our simulation is based on data obtained from two self versus self microarray experiments and is free of distributional assumptions. Our results identify factors that are important for determining whether to background subtract, including the correlation of foreground to background intensity ratios. Using these results we develop recommendations for diagnostic visualizations that can help decisions about background subtraction.


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