TY - JOUR
T1 - When should one subtract background fluorescence in 2-color microarrays?
AU - Scharpf, Robert B.
AU - Iacobuzio-Donahue, Christine A.
AU - Sneddon, Julie B.
AU - Parmigiani, Giovanni
PY - 2007/10
Y1 - 2007/10
N2 - 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.
AB - 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.
KW - Background substraction
KW - Microarray
UR - http://www.scopus.com/inward/record.url?scp=34547659833&partnerID=8YFLogxK
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U2 - 10.1093/biostatistics/kxl041
DO - 10.1093/biostatistics/kxl041
M3 - Article
C2 - 17164465
AN - SCOPUS:34547659833
VL - 8
SP - 695
EP - 707
JO - Biostatistics
JF - Biostatistics
SN - 1465-4644
IS - 4
ER -