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

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...

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Bibliographic Details
Published in:Bioinformatics (Oxford, England) England), 2003-01, Vol.19 (2), p.185-193
Main Authors: BOLSTAD, B. M, IRIZARRY, R. A, ASTRAND, M, SPEED, T. P
Format: Article
Language:English
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
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Summary: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. 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. 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. Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html
ISSN:1367-4803
1367-4811
DOI:10.1093/bioinformatics/19.2.185