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Iterative rank-order normalization of gene expression microarray data

BACKGROUND: Many gene expression normalization algorithms exist for Affymetrix GeneChip microarrays. The most popular of these is RMA, primarily due to the precision and low noise produced during the process. A significant strength of this and similar approaches is the use of the entire set of array...

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Autores principales: Welsh, Eric A, Eschrich, Steven A, Berglund, Anders E, Fenstermacher, David A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3651355/
https://www.ncbi.nlm.nih.gov/pubmed/23647742
http://dx.doi.org/10.1186/1471-2105-14-153
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author Welsh, Eric A
Eschrich, Steven A
Berglund, Anders E
Fenstermacher, David A
author_facet Welsh, Eric A
Eschrich, Steven A
Berglund, Anders E
Fenstermacher, David A
author_sort Welsh, Eric A
collection PubMed
description BACKGROUND: Many gene expression normalization algorithms exist for Affymetrix GeneChip microarrays. The most popular of these is RMA, primarily due to the precision and low noise produced during the process. A significant strength of this and similar approaches is the use of the entire set of arrays during both normalization and model-based estimation of signal. However, this leads to differing estimates of expression based on the starting set of arrays, and estimates can change when a single, additional chip is added to the set. Additionally, outlier chips can impact the signals of other arrays, and can themselves be skewed by the majority of the population. RESULTS: We developed an approach, termed IRON, which uses the best-performing techniques from each of several popular processing methods while retaining the ability to incrementally renormalize data without altering previously normalized expression. This combination of approaches results in a method that performs comparably to existing approaches on artificial benchmark datasets (i.e. spike-in) and demonstrates promising improvements in segregating true signals within biologically complex experiments. CONCLUSIONS: By combining approaches from existing normalization techniques, the IRON method offers several advantages. First, IRON normalization occurs pair-wise, thereby avoiding the need for all chips to be normalized together, which can be important for large data analyses. Secondly, the technique does not require similarity in signal distribution across chips for normalization, which can be important for maintaining biologically relevant differences in a heterogeneous background. Lastly, IRON introduces fewer post-processing artifacts, particularly in data whose behavior violates common assumptions. Thus, the IRON method provides a practical solution to common needs of expression analysis. A software implementation of IRON is available at [http://gene.moffitt.org/libaffy/].
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spelling pubmed-36513552013-05-11 Iterative rank-order normalization of gene expression microarray data Welsh, Eric A Eschrich, Steven A Berglund, Anders E Fenstermacher, David A BMC Bioinformatics Methodology Article BACKGROUND: Many gene expression normalization algorithms exist for Affymetrix GeneChip microarrays. The most popular of these is RMA, primarily due to the precision and low noise produced during the process. A significant strength of this and similar approaches is the use of the entire set of arrays during both normalization and model-based estimation of signal. However, this leads to differing estimates of expression based on the starting set of arrays, and estimates can change when a single, additional chip is added to the set. Additionally, outlier chips can impact the signals of other arrays, and can themselves be skewed by the majority of the population. RESULTS: We developed an approach, termed IRON, which uses the best-performing techniques from each of several popular processing methods while retaining the ability to incrementally renormalize data without altering previously normalized expression. This combination of approaches results in a method that performs comparably to existing approaches on artificial benchmark datasets (i.e. spike-in) and demonstrates promising improvements in segregating true signals within biologically complex experiments. CONCLUSIONS: By combining approaches from existing normalization techniques, the IRON method offers several advantages. First, IRON normalization occurs pair-wise, thereby avoiding the need for all chips to be normalized together, which can be important for large data analyses. Secondly, the technique does not require similarity in signal distribution across chips for normalization, which can be important for maintaining biologically relevant differences in a heterogeneous background. Lastly, IRON introduces fewer post-processing artifacts, particularly in data whose behavior violates common assumptions. Thus, the IRON method provides a practical solution to common needs of expression analysis. A software implementation of IRON is available at [http://gene.moffitt.org/libaffy/]. BioMed Central 2013-05-07 /pmc/articles/PMC3651355/ /pubmed/23647742 http://dx.doi.org/10.1186/1471-2105-14-153 Text en Copyright © 2013 Welsh et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Welsh, Eric A
Eschrich, Steven A
Berglund, Anders E
Fenstermacher, David A
Iterative rank-order normalization of gene expression microarray data
title Iterative rank-order normalization of gene expression microarray data
title_full Iterative rank-order normalization of gene expression microarray data
title_fullStr Iterative rank-order normalization of gene expression microarray data
title_full_unstemmed Iterative rank-order normalization of gene expression microarray data
title_short Iterative rank-order normalization of gene expression microarray data
title_sort iterative rank-order normalization of gene expression microarray data
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3651355/
https://www.ncbi.nlm.nih.gov/pubmed/23647742
http://dx.doi.org/10.1186/1471-2105-14-153
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