A comparison of background correction methods for two-colour microarrays
Open accessAbstract
MOTIVATION: 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. AVAILABILITY: The background correction methods compared in this article are available in the R package limma (Smyth, 2005) from http://www.bioconductor.org. SUPPLEMENTARY INFORMATION: Supplementary data are available from http://bioinf.wehi.edu.au/resources/webReferences.html.
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The guiding principle in the pre-processing steps is to preserve information, avoiding missing values or inflated variances.
“The guiding principle in the pre-processing steps is to preserve information, avoiding missing values or inflated variances (23).”
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Cites
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829 · see citing works
Self-citationIn silico prediction of protein-protein interactions in human macrophages
Souiai, O., Guerfali, F. Z., Miled, S. B., Brun, C., & Benkahla, A. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-157 · Cited by 10,785 · see citing works
Exploration, normalization, and summaries of high density oligonucleotide array probe level data
Irizarry, R. A. (2003) · Biostatistics · doi:10.1093/biostatistics/4.2.249 · Cited by 10,603 · see citing works
Significance analysis of microarrays applied to the ionizing radiation response
Tusher, V. G., Tibshirani, R., & Chu, G. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.091062498 · Cited by 10,568 · see citing works
limma: Linear Models for Microarray Data
Smyth, G. K. (2005) · Statistics for Biology and Health · doi:10.1007/0-387-29362-0_23 · Cited by 5,796 · see citing works
Self-citationBioinformatics and Computational Biology Solutions Using R and Bioconductor
Gentleman, R. C., Carey, V. J., Huber, W. K., Irizarry, R. A., & Dudoit, S. (2005) · Statistics in the health sciences · doi:10.1007/0-387-29362-0 · Cited by 3,470 · see citing works
Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., & Vingron, M. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s96 · Cited by 2,440 · see citing works
Use of within-array replicate spots for assessing differential expression in microarray experiments
Smyth, G. K., Michaud, J., & Scott, H. S. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti270 · Cited by 1,406 · see citing works
Self-citationNewton, M. A., Kendziorski, C. M., Richmond, C. S., Blattner, F. R., & Tsui, K. (2001) · Journal of Computational Biology · doi:10.1089/106652701300099074 · Cited by 739 · see citing works
Peart, M. J., Smyth, G. K., van Laar, R. K., Bowtell, D. D. L., Richon, V. M., Marks, P. A., Holloway, A. J., & Johnstone, R. W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500369102 · Cited by 538 · see citing works
Self-citationA variance-stabilizing transformation for gene-expression microarray data
Durbin, B. P., Hardin, J. S., Hawkins, D. M., & Rocke, D. M. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s105 · Cited by 470 · see citing works
Comparison of Methods for Image Analysis on cDNA Microarray Data
Yang, J. Y. H., Buckley, M. J., Dudoit, S., & Speed, T. P. (2002) · Journal of Computational and Graphical Statistics · doi:10.1198/106186002317375640 · Cited by 456 · see citing works
Normalization for cDNA microarry data
Yang, J. Y. H., Dudoit, S., Luu, P., & Speed, T. P. (2001) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.427982 · Cited by 442 · see citing works
Within the fold: assessing differential expression measures and reproducibility in microarray assays
Yang, I. V., Chen, E., Hasseman, J. P., Liang, W. P., Frank, B. C., Wang, S., Sharov, V., Saeed, A. I., White, J., Li, J., et al. (2002) · Genome biology · doi:10.1186/gb-2002-3-11-research0062 · Cited by 336 · see citing works
Expression profiling in primates reveals a rapid evolution of human transcription factors
Gilad, Y., Oshlack, A., Smyth, G. K., Speed, T. P., & White, K. P. (2006) · Nature · doi:10.1038/nature04559 · Cited by 323 · see citing works
Self-citationAnalysis of cDNA microarray images
Yang, J. Y. H. (2001) · Briefings in Bioinformatics · doi:10.1093/bib/2.4.341 · Cited by 208 · see citing works
Approximate variance-stabilizing transformations for gene-expression microarray data
Rocke, D. M., & Durbin, B. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg107 · Cited by 160 · see citing works
Processing and quality control of DNA array hybridization data
Beißbarth, T., Fellenberg, K., Brors, B., et al. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.11.1014 · Cited by 153 · see citing works
Non-linear normalization and background correction in one-channel cDNA microarray studies
Edwards, D. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg083 · Cited by 144 · see citing works
Improved Background Correction for Spotted DNA Microarrays
Kooperberg, C. L., Fazzio, T. G., Delrow, J. J., & Tsukiyama, T. (2002) · Journal of Computational Biology · doi:10.1089/10665270252833190 · Cited by 128 · see citing works
Transformations for cDNA Microarray Data
Cui, X., Kerr, K. F., & Churchill, G. A. (2003) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1009 · Cited by 123 · see citing works
Tran, P. H. (2002) · Nucleic Acids Research · doi:10.1093/nar/gnf053 · Cited by 119 · see citing works
Identifying Differentially Expressed Genes in cDNA Microarray Experiments
Baggerly, K., Coombes, K. R., Hess, K. R., et al. (2001) · Journal of Computational Biology · doi:10.1089/106652701753307539 · Cited by 109 · see citing works
Microarray data quality analysis: lessons from the AFGC project
FINKELSTEIN, D. B., Ewing, R. M., Gollub, J., et al. (2002) · Plant Molecular Biology · doi:10.1023/a:1013765922672 · Cited by 87 · see citing works
Empirical evaluation of data transformations and ranking statistics for microarray analysis
Qin, L. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh866 · Cited by 83 · see citing works
Normalization for c{DNA} microarray data
Yang, Y. H., Dudoit, S., Luu, P., et al. (2001) · UC Berkeley · Cited by 72 · see citing works
Normalization for cDNA Microarray Data
Kim, D. H., Choi, S. W., Park, J. H., et al. (2002) · Cited by 65 · see citing works
Background correction for cDNA microarray images using the TV+L1 model
Yin, W., Chen, T., Zhou, S. X., et al. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti341 · Cited by 64 · see citing works
Variance-stabilizing transformations for two-color microarrays
Durbin, B., & Rocke, D. M. (2004) · Bioinformatics · doi:10.1093/bioinformatics/btg464 · Cited by 56 · see citing works
Defining signal thresholds in DNA microarrays: exemplary application for invasive cancer
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McGee, M., & Chen, Z. (2006) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1237 · Cited by 39 · see citing works
Holloway, A. J., Oshlack, A., Diyagama, D. S., Bowtell, D. D. L., & Smyth, G. K. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-511 · Cited by 27 · see citing works
Self-citationKafadar, K., & Phang, T. (2003) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(03)00069-0 · Cited by 9 · see citing works
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- Authors
- Ritchie, M. E., Silver, J., Oshlack, A., Holmes, M., Diyagama, D., Holloway, A., & Smyth, G. K.
- Affiliations
- Walter and Eliza Hall Institute of Medical Research (AU)
- Corresponding author
- Smyth, Gordon K.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-08-25
- Volume
- 23
- Issue
- 20
- Pages
- 2700-2707
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2164216268
- Cited by
- 907
- References
- 33
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 21 · 2023: 27 · 2022: 17 · 2021: 26 · 2020: 44 · 2019: 34 · 2018: 34 · 2017: 43 · 2016: 66 · 2015: 80 · 2014: 76 · 2013: 115 · 2012: 85
- FWCI
- 19.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- background correction, differential gene expression, convolution model, false discovery rate, SAM, morphs
- MeSH terms
- Image Enhancement, Image Interpretation, Computer-Assisted, Sensitivity and Specificity, Reproducibility of Results, Artifacts, In Situ Hybridization, Fluorescence, Oligonucleotide Array Sequence Analysis, Microscopy, Fluorescence, Multiphoton
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