A comparison of background correction methods for two-colour microarrays.
BACKGROUND
Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments.


RESULTS
Using data where some independent truth in gene expression is known, eight different background correction alternatives are compared, in terms of precision and bias of the resulting gene expression measures, and in terms of their ability to detect differentially expressed genes as judged by two popular algorithms, SAM and limma eBayes. A new background processing method (normexp) is introduced which is based on a convolution model. The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates. Methods which stabilize the variances of the log-ratios along the intensity range perform the best. The normexp+offset method is found to give the lowest false discovery rate overall, followed by morph and vsn. Like vsn, normexp is applicable to most types of two-colour microarray data.


BACKGROUND
The background correction methods compared in this article are available in the R package limma (Smyth, 2005) from http://www.bioconductor.org.


BACKGROUND
Supplementary data are available from http://bioinf.wehi.edu.au/resources/webReferences.html.
Ritchie
Matthew E
ME
Department of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.
Silver
Jeremy
J
Oshlack
Alicia
A
Holmes
Melissa
M
Diyagama
Dileepa
D
Holloway
Andrew
A
Smyth
Gordon K
GK
D016477:Artifacts; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D017404:In Situ Hybridization, Fluorescence / Q000379:methods*; D036641:Microscopy, Fluorescence, Multiphoton / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity