A comparison of normalization methods for high density oligonucleotide array data based on variance and bias

• Published in: Bioinformatics (Oxford, England) Vol. 19; no. 2; pp. 185 - 193
• Main Authors: Bolstad, B.M., Irizarry, R.A, Åstrand, M., Speed, T.P.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 22.01.2003; Oxford Publishing Limited (England)
• Subjects: Algorithms; Biological and medical sciences; Calibration; Fundamental and applied biological sciences. Psychology; General aspects; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Genetic; Molecular Probes; Nonlinear Dynamics; Oligonucleotide Array Sequence Analysis - instrumentation; Oligonucleotide Array Sequence Analysis - methods; Oligonucleotide Array Sequence Analysis - standards; Quality Control; Sequence Analysis, DNA - methods; Sequence Analysis, DNA - standards; Stochastic Processes; Bias; Standardization; DNA chip; Web site; Bioinformatics; Algorithm; Comparative study; Variance
• ISSN: 1367-4803; 1367-4811; 1367-4811
• DOI: 10.1093/bioinformatics/19.2.185

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@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.