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Integrative analysis of gene expression and copy number alterations using canonical correlation analysis

BACKGROUND: With the rapid development of new genetic measurement methods, several types of genetic alterations can be quantified in a high-throughput manner. While the initial focus has been on investigating each data set separately, there is an increasing interest in studying the correlation struc...

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Autores principales: Soneson, Charlotte, Lilljebjörn, Henrik, Fioretos, Thoas, Fontes, Magnus
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2873536/
https://www.ncbi.nlm.nih.gov/pubmed/20398334
http://dx.doi.org/10.1186/1471-2105-11-191
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author Soneson, Charlotte
Lilljebjörn, Henrik
Fioretos, Thoas
Fontes, Magnus
author_facet Soneson, Charlotte
Lilljebjörn, Henrik
Fioretos, Thoas
Fontes, Magnus
author_sort Soneson, Charlotte
collection PubMed
description BACKGROUND: With the rapid development of new genetic measurement methods, several types of genetic alterations can be quantified in a high-throughput manner. While the initial focus has been on investigating each data set separately, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods based on Canonical Correlation Analysis (CCA) have been proposed for integrating paired genetic data sets. The high dimensionality of microarray data imposes computational difficulties, which have been addressed for instance by studying the covariance structure of the data, or by reducing the number of variables prior to applying the CCA. In this work, we propose a new method for analyzing high-dimensional paired genetic data sets, which mainly emphasizes the correlation structure and still permits efficient application to very large data sets. The method is implemented by translating a regularized CCA to its dual form, where the computational complexity depends mainly on the number of samples instead of the number of variables. The optimal regularization parameters are chosen by cross-validation. We apply the regularized dual CCA, as well as a classical CCA preceded by a dimension-reducing Principal Components Analysis (PCA), to a paired data set of gene expression changes and copy number alterations in leukemia. RESULTS: Using the correlation-maximizing methods, regularized dual CCA and PCA+CCA, we show that without pre-selection of known disease-relevant genes, and without using information about clinical class membership, an exploratory analysis singles out two patient groups, corresponding to well-known leukemia subtypes. Furthermore, the variables showing the highest relevance to the extracted features agree with previous biological knowledge concerning copy number alterations and gene expression changes in these subtypes. Finally, the correlation-maximizing methods are shown to yield results which are more biologically interpretable than those resulting from a covariance-maximizing method, and provide different insight compared to when each variable set is studied separately using PCA. CONCLUSIONS: We conclude that regularized dual CCA as well as PCA+CCA are useful methods for exploratory analysis of paired genetic data sets, and can be efficiently implemented also when the number of variables is very large.
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spelling pubmed-28735362010-05-20 Integrative analysis of gene expression and copy number alterations using canonical correlation analysis Soneson, Charlotte Lilljebjörn, Henrik Fioretos, Thoas Fontes, Magnus BMC Bioinformatics Research article BACKGROUND: With the rapid development of new genetic measurement methods, several types of genetic alterations can be quantified in a high-throughput manner. While the initial focus has been on investigating each data set separately, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods based on Canonical Correlation Analysis (CCA) have been proposed for integrating paired genetic data sets. The high dimensionality of microarray data imposes computational difficulties, which have been addressed for instance by studying the covariance structure of the data, or by reducing the number of variables prior to applying the CCA. In this work, we propose a new method for analyzing high-dimensional paired genetic data sets, which mainly emphasizes the correlation structure and still permits efficient application to very large data sets. The method is implemented by translating a regularized CCA to its dual form, where the computational complexity depends mainly on the number of samples instead of the number of variables. The optimal regularization parameters are chosen by cross-validation. We apply the regularized dual CCA, as well as a classical CCA preceded by a dimension-reducing Principal Components Analysis (PCA), to a paired data set of gene expression changes and copy number alterations in leukemia. RESULTS: Using the correlation-maximizing methods, regularized dual CCA and PCA+CCA, we show that without pre-selection of known disease-relevant genes, and without using information about clinical class membership, an exploratory analysis singles out two patient groups, corresponding to well-known leukemia subtypes. Furthermore, the variables showing the highest relevance to the extracted features agree with previous biological knowledge concerning copy number alterations and gene expression changes in these subtypes. Finally, the correlation-maximizing methods are shown to yield results which are more biologically interpretable than those resulting from a covariance-maximizing method, and provide different insight compared to when each variable set is studied separately using PCA. CONCLUSIONS: We conclude that regularized dual CCA as well as PCA+CCA are useful methods for exploratory analysis of paired genetic data sets, and can be efficiently implemented also when the number of variables is very large. BioMed Central 2010-04-15 /pmc/articles/PMC2873536/ /pubmed/20398334 http://dx.doi.org/10.1186/1471-2105-11-191 Text en Copyright ©2010 Soneson 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 Research article
Soneson, Charlotte
Lilljebjörn, Henrik
Fioretos, Thoas
Fontes, Magnus
Integrative analysis of gene expression and copy number alterations using canonical correlation analysis
title Integrative analysis of gene expression and copy number alterations using canonical correlation analysis
title_full Integrative analysis of gene expression and copy number alterations using canonical correlation analysis
title_fullStr Integrative analysis of gene expression and copy number alterations using canonical correlation analysis
title_full_unstemmed Integrative analysis of gene expression and copy number alterations using canonical correlation analysis
title_short Integrative analysis of gene expression and copy number alterations using canonical correlation analysis
title_sort integrative analysis of gene expression and copy number alterations using canonical correlation analysis
topic Research article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2873536/
https://www.ncbi.nlm.nih.gov/pubmed/20398334
http://dx.doi.org/10.1186/1471-2105-11-191
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