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

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...

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Bibliographic Details
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
Online Access	Get full text
ISSN	1367-4803 1367-4811 1367-4811
DOI	10.1093/bioinformatics/19.2.185

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Abstract	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.
AbstractList	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. 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 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 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.MOTIVATIONWhen 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.RESULTSWe 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.AVAILABILITYSoftware 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.htmlSUPPLEMENTARY INFORMATIONAdditional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html 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.
Author	Irizarry, R.A Åstrand, M. Bolstad, B.M. Speed, T.P.
Author_xml	– sequence: 1 givenname: B.M. surname: Bolstad fullname: Bolstad, B.M. – sequence: 2 givenname: R.A surname: Irizarry fullname: Irizarry, R.A – sequence: 3 givenname: M. surname: Åstrand fullname: Åstrand, M. – sequence: 4 givenname: T.P. surname: Speed fullname: Speed, T.P.
BackLink	http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=14573510$$DView record in Pascal Francis https://www.ncbi.nlm.nih.gov/pubmed/12538238$$D View this record in MEDLINE/PubMed
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Snippet	Motivation: When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays... When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of...
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SubjectTerms	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
Title	A comparison of normalization methods for high density oligonucleotide array data based on variance and bias
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