title: When should one subtract background fluorescence in 2-color microarrays?
abstract: 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 nonspecific 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 2 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.
authors.affiliation: Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. rscharpf@jhsph.edu.
authors.forename: Robert B
authors.initials: RB
authors.lastname: Scharpf
authors.forename: Christine A
authors.initials: CA
authors.lastname: Iacobuzio-Donahue
authors.forename: Julie B
authors.initials: JB
authors.lastname: Sneddon
authors.forename: Giovanni
authors.initials: G
authors.lastname: Parmigiani
mesh_terms: D000465:Algorithms; D001699:Biometry; D030541:Databases, Genetic / Q000706:statistics & numerical data; D005453:Fluorescence; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000706:statistics & numerical data*