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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W. K. Nevala, C. M. Vachon, A. A. Leontovich, C. G. Scott, M. A. Thompson, S. N. Markovic, and for the Melanoma Study Group of the Mayo Clinic Ca Evidence of Systemic Th2-Driven Chronic Inflammation in Patients with Metastatic Melanoma Clin. Cancer Res., March 15, 2009; 15(6): 1931 - 1939. [Abstract] [Full Text] [PDF]
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F. Friedrichs, C. Zugck, G.-J. Rauch, B. Ivandic, D. Weichenhan, M. Muller-Bardorff, B. Meder, N. E. El Mokhtari, V. Regitz-Zagrosek, R. Hetzer, et al. HBEGF, SRA1, and IK: Three cosegregating genes as determinants of cardiomyopathy Genome Res., March 1, 2009; 19(3): 395 - 403. [Abstract] [Full Text] [PDF]
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S. Alon, E. Eisenberg, J. Jacob-Hirsch, G. Rechavi, G. Vatine, R. Toyama, S. L. Coon, D. C. Klein, and Y. Gothilf A new cis-acting regulatory element driving gene expression in the zebrafish pineal gland Bioinformatics, March 1, 2009; 25(5): 559 - 562. [Abstract] [Full Text] [PDF]
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A. Sassano, M. Lo Iacono, G. Antico, A. Jordan, S. Uddin, R. A. Calogero, and L. C. Platanias Regulation of leukemic cell differentiation and retinoid-induced gene expression by statins Mol. Cancer Ther., March 1, 2009; 8(3): 615 - 625. [Abstract] [Full Text] [PDF]
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D. Chandran, Y. C. Tai, G. Hather, J. Dewdney, C. Denoux, D. G. Burgess, F. M. Ausubel, T. P. Speed, and M. C. Wildermuth Temporal Global Expression Data Reveal Known and Novel Salicylate-Impacted Processes and Regulators Mediating Powdery Mildew Growth and Reproduction on Arabidopsis Plant Physiology, March 1, 2009; 149(3): 1435 - 1451. [Abstract] [Full Text] [PDF]
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Y. Merbl and M. W. Kirschner Large-scale detection of ubiquitination substrates using cell extracts and protein microarrays PNAS, February 24, 2009; 106(8): 2543 - 2548. [Abstract] [Full Text] [PDF]
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Y. Zhang, J. Wong, M. Klinger, M. T. Tran, K. M. Shannon, and N. Killeen Mll5 contributes to hematopoietic stem cell fitness and homeostasis Blood, February 12, 2009; 113(7): 1455 - 1463. [Abstract] [Full Text] [PDF]
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N. P. Smirnova, A. A. Ptitsyn, K. J. Austin, H. Bielefeldt-Ohmann, H. Van Campen, H. Han, A. L. van Olphen, and T. R. Hansen Persistent fetal infection with bovine viral diarrhea virus differentially affects maternal blood cell signal transduction pathways Physiol Genomics, February 2, 2009; 36(3): 129 - 139. [Abstract] [Full Text] [PDF]
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 D. L. Allen, E. R. Bandstra, B. C. Harrison, S. Thorng, L. S. Stodieck, P. J. Kostenuik, S. Morony, D. L. Lacey, T. G. Hammond, L. L. Leinwand, et al. Effects of spaceflight on murine skeletal muscle gene expression J Appl Physiol, February 1, 2009; 106(2): 582 - 595. [Abstract] [Full Text] [PDF]
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S. Pounds, C. Cheng, C. Mullighan, S. C. Raimondi, S. Shurtleff, and J. R. Downing Reference alignment of SNP microarray signals for copy number analysis of tumors Bioinformatics, February 1, 2009; 25(3): 315 - 321. [Abstract] [Full Text] [PDF]
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K. H. Shain, D. N. Yarde, M. B. Meads, M. Huang, R. Jove, L. A. Hazlehurst, and W. S. Dalton {beta}1 Integrin Adhesion Enhances IL-6-Mediated STAT3 Signaling in Myeloma Cells: Implications for Microenvironment Influence on Tumor Survival and Proliferation Cancer Res., February 1, 2009; 69(3): 1009 - 1015. [Abstract] [Full Text] [PDF]
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H. Su, J.-R. Yang, T. Xu, J. Huang, L. Xu, Y. Yuan, and S.-M. Zhuang MicroRNA-101, Down-regulated in Hepatocellular Carcinoma, Promotes Apoptosis and Suppresses Tumorigenicity Cancer Res., February 1, 2009; 69(3): 1135 - 1142. [Abstract] [Full Text] [PDF]
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T. Akagi, S. Ogawa, M. Dugas, N. Kawamata, G. Yamamoto, Y. Nannya, M. Sanada, C. W. Miller, A. Yung, S. Schnittger, et al. Frequent genomic abnormalities in acute myeloid leukemia/myelodysplastic syndrome with normal karyotype Haematologica, February 1, 2009; 94(2): 213 - 223. [Abstract] [Full Text] [PDF]
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