Bioinformatics Vol. 19 no. 2 2003
Pages 185-193
© 2003 Oxford University Press
A comparison of normalization methods for high density oligonucleotide array data based on variance and bias
1 Group in Biostatistics, University of California, Berkeley, CA 94720, USA
2 Department of Biostatistics, John Hopkins University, Baltimore, MD, USA
3 AstraZeneca R & D Mölndal, Sweden
4 Department of Statistics, University of California, Berkeley, CA 94720, USA
5 Division of Genetics and Bioinformatics, WEHI, Melbourne, Australia
Received on June 13, 2002
; revised on September 11, 2002
; accepted on September 17, 2002
Motivation: When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations.
Results: We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably.
Availability: Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org.
Contact: bolstad{at}stat.berkeley.edu.
Supplementary information: Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html
* To whom correspondence should be addressed.
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J. Thiel, D. Weier, N. Sreenivasulu, M. Strickert, N. Weichert, M. Melzer, T. Czauderna, U. Wobus, H. Weber, and W. Weschke Different Hormonal Regulation of Cellular Differentiation and Function in Nucellar Projection and Endosperm Transfer Cells: A Microdissection-Based Transcriptome Study of Young Barley Grains Plant Physiology, November 1, 2008; 148(3): 1436 - 1452. [Abstract] [Full Text] [PDF]
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S. P. Rius, P. Casati, A. A. Iglesias, and D. F. Gomez-Casati Characterization of Arabidopsis Lines Deficient in GAPC-1, a Cytosolic NAD-Dependent Glyceraldehyde-3-Phosphate Dehydrogenase Plant Physiology, November 1, 2008; 148(3): 1655 - 1667. [Abstract] [Full Text] [PDF]
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N. E. Pagliaccetti, R. Eduardo, S. H. Kleinstein, X. J. Mu, P. Bandi, and M. D. Robek Interleukin-29 Functions Cooperatively with Interferon to Induce Antiviral Gene Expression and Inhibit Hepatitis C Virus Replication J. Biol. Chem., October 31, 2008; 283(44): 30079 - 30089. [Abstract] [Full Text] [PDF]
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J. R. Starkey, A. K. Rebane, M. A. Drobizhev, F. Meng, A. Gong, A. Elliott, K. McInnerney, and C. W. Spangler New Two-Photon Activated Photodynamic Therapy Sensitizers Induce Xenograft Tumor Regressions after Near-IR Laser Treatment through the Body of the Host Mouse Clin. Cancer Res., October 15, 2008; 14(20): 6564 - 6573. [Abstract] [Full Text] [PDF]
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A. Bye, M. Langaas, M. A. Hoydal, O. J. Kemi, G. Heinrich, L. G. Koch, S. L. Britton, S. M. Najjar, O. Ellingsen, and U. Wisloff Aerobic capacity-dependent differences in cardiac gene expression Physiol Genomics, October 8, 2008; 33(1): 100 - 109. [Abstract] [Full Text] [PDF]
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I. Alam, Q. Sun, L. Liu, D. L. Koller, Y. Liu, H. J. Edenberg, M. J. Econs, T. Foroud, and C. H. Turner Genomic expression analysis of rat chromosome 4 for skeletal traits at femoral neck Physiol Genomics, October 8, 2008; 35(2): 191 - 196. [Abstract] [Full Text] [PDF]
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 C. H. Jakobsen, G. L. Storvold, H. Bremseth, T. Follestad, K. Sand, M. Mack, K. S. Olsen, A. G. Lundemo, J. G. Iversen, H. E. Krokan, et al. DHA induces ER stress and growth arrest in human colon cancer cells: associations with cholesterol and calcium homeostasis J. Lipid Res., October 1, 2008; 49(10): 2089 - 2100. [Abstract] [Full Text] [PDF]
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G. Thillainadesan, M. Isovic, E. Loney, J. Andrews, M. Tini, and J. Torchia Genome Analysis Identifies the p15ink4b Tumor Suppressor as a Direct Target of the ZNF217/CoREST Complex Mol. Cell. Biol., October 1, 2008; 28(19): 6066 - 6077. [Abstract] [Full Text] [PDF]
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E. Giannoulatou, C. Yau, S. Colella, J. Ragoussis, and C. C. Holmes GenoSNP: a variational Bayes within-sample SNP genotyping algorithm that does not require a reference population Bioinformatics, October 1, 2008; 24(19): 2209 - 2214. [Abstract] [Full Text] [PDF]
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R. Tomasini, K. Tsuchihara, M. Wilhelm, M. Fujitani, A. Rufini, C. C. Cheung, F. Khan, A. Itie-Youten, A. Wakeham, M.-s. Tsao, et al. TAp73 knockout shows genomic instability with infertility and tumor suppressor functions Genes & Dev., October 1, 2008; 22(19): 2677 - 2691. [Abstract] [Full Text] [PDF]
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 C. Pantoja, J. T. Huff, and K. R. Yamamoto Glucocorticoid Signaling Defines a Novel Commitment State during Adipogenesis In Vitro Mol. Biol. Cell, October 1, 2008; 19(10): 4032 - 4041. [Abstract] [Full Text] [PDF]
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S. Clasper, D. Royston, D. Baban, Y. Cao, S. Ewers, S. Butz, D. Vestweber, and D. G. Jackson A Novel Gene Expression Profile in Lymphatics Associated with Tumor Growth and Nodal Metastasis Cancer Res., September 15, 2008; 68(18): 7293 - 7303. [Abstract] [Full Text] [PDF]
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 M. Wang, J. You, K. G. Bemis, T. J. Tegeler, and D. P. G. Brown Label-free mass spectrometry-based protein quantification technologies in proteomic analysis Brief Funct Genomic Proteomic, September 1, 2008; 7(5): 329 - 339. [Abstract] [Full Text] [PDF]
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 A. F. Alice, H. Naka, and J. H. Crosa Global Gene Expression as a Function of the Iron Status of the Bacterial Cell: Influence of Differentially Expressed Genes in the Virulence of the Human Pathogen Vibrio vulnificus Infect. Immun., September 1, 2008; 76(9): 4019 - 4037. [Abstract] [Full Text] [PDF]
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I. Michael, M. Shmoish, D. S. Walton, and S. Levenberg Interactions between Trabecular Meshwork Cells and Lens Epithelial Cells: A Possible Mechanism in Infantile Aphakic Glaucoma Invest. Ophthalmol. Vis. Sci., September 1, 2008; 49(9): 3981 - 3987. [Abstract] [Full Text] [PDF]
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 G. G. Fernandez-Ranvier, J. Weng, R.-F. Yeh, E. Khanafshar, I. Suh, C. Barker, Q. Y. Duh, O. H. Clark, and E. Kebebew Identification of Biomarkers of Adrenocortical Carcinoma Using Genomewide Gene Expression Profiling Arch Surg, September 1, 2008; 143(9): 841 - 846. [Abstract] [Full Text] [PDF]
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B. N. Kalali, G. Kollisch, J. Mages, T. Muller, S. Bauer, H. Wagner, J. Ring, R. Lang, M. Mempel, and M. Ollert Double-Stranded RNA Induces an Antiviral Defense Status in Epidermal Keratinocytes through TLR3-, PKR-, and MDA5/RIG-I-Mediated Differential Signaling J. Immunol., August 15, 2008; 181(4): 2694 - 2704. [Abstract] [Full Text] [PDF]
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R. W. Tothill, A. V. Tinker, J. George, R. Brown, S. B. Fox, S. Lade, D. S. Johnson, M. K. Trivett, D. Etemadmoghadam, B. Locandro, et al. Novel Molecular Subtypes of Serous and Endometrioid Ovarian Cancer Linked to Clinical Outcome Clin. Cancer Res., August 15, 2008; 14(16): 5198 - 5208. [Abstract] [Full Text] [PDF]
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H.-I H. Chen, F.-H. Hsu, Y. Jiang, M.-H. Tsai, P.-C. Yang, P. S. Meltzer, E. Y. Chuang, and Y. Chen A probe-density-based analysis method for array CGH data: simulation, normalization and centralization Bioinformatics, August 15, 2008; 24(16): 1749 - 1756. [Abstract] [Full Text] [PDF]
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