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Open Access Highly Accessed Research article

Pre-processing Agilent microarray data

Marianna Zahurak1*, Giovanni Parmigiani1, Wayne Yu2, Robert B Scharpf3, David Berman4, Edward Schaeffer5, Shabana Shabbeer6 and Leslie Cope1

Author Affiliations

1 Johns Hopkins University School of Medicine, Oncology Biostatistics, 550 N. Broadway, Baltimore, MD 21205, USA

2 Johns Hopkins School of Medicine, Baltimore, MD 21231, USA

3 Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Room E3034, Baltimore, MD 21205, USA

4 Johns Hopkins University School of Medicine, 1550 Orleans St., CRB II Room 5.45, Baltimore, MD 21231, USA

5 Johns Hopkins University School of Medicine, 600 N. Wolfe St., Marburg 145, Baltimore, MD 21287, USA

6 Johns Hopkins University School of Medicine, 1650 Orleans St., CRB I, Baltimore, MD 21231, USA

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BMC Bioinformatics 2007, 8:142  doi:10.1186/1471-2105-8-142

Published: 1 May 2007

Abstract

Background

Pre-processing methods for two-sample long oligonucleotide arrays, specifically the Agilent technology, have not been extensively studied. The goal of this study is to quantify some of the sources of error that affect measurement of expression using Agilent arrays and to compare Agilent's Feature Extraction software with pre-processing methods that have become the standard for normalization of cDNA arrays. These include log transformation followed by loess normalization with or without background subtraction and often a between array scale normalization procedure. The larger goal is to define best study design and pre-processing practices for Agilent arrays, and we offer some suggestions.

Results

Simple loess normalization without background subtraction produced the lowest variability. However, without background subtraction, fold changes were biased towards zero, particularly at low intensities. ROC analysis of a spike-in experiment showed that differentially expressed genes are most reliably detected when background is not subtracted. Loess normalization and no background subtraction yielded an AUC of 99.7% compared with 88.8% for Agilent processed fold changes. All methods performed well when error was taken into account by t- or z-statistics, AUCs ≥ 99.8%. A substantial proportion of genes showed dye effects, 43% (99%CI : 39%, 47%). However, these effects were generally small regardless of the pre-processing method.

Conclusion

Simple loess normalization without background subtraction resulted in low variance fold changes that more reliably ranked gene expression than the other methods. While t-statistics and other measures that take variation into account, including Agilent's z-statistic, can also be used to reliably select differentially expressed genes, fold changes are a standard measure of differential expression for exploratory work, cross platform comparison, and biological interpretation and can not be entirely replaced. Although dye effects are small for most genes, many array features are affected. Therefore, an experimental design that incorporates dye swaps or a common reference could be valuable.