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

Soneson, Charlotte LU ; Lilljebjörn, Henrik LU ; Fioretos, Thoas LU and Fontes, Magnus LU (2010) In BMC Bioinformatics 11(191). p.1-20
Abstract
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
BMC Bioinformatics
volume
11
issue
191
pages
1 - 20
publisher
BioMed Central
external identifiers
  • wos:000277793400002
  • scopus:77950792775
ISSN
1471-2105
DOI
10.1186/1471-2105-11-191
language
English
LU publication?
yes
id
c44f1c40-5a35-435f-9d4d-7d9bf07d0dfc (old id 1599754)
alternative location
http://www.biomedcentral.com/1471-2105/11/191/abstract
date added to LUP
2010-05-24 14:24:42
date last changed
2018-07-08 03:40:51
@article{c44f1c40-5a35-435f-9d4d-7d9bf07d0dfc,
  abstract     = {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. <br/><br>
 <br/><br>
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.<br/><br>
<br/><br>
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.},
  author       = {Soneson, Charlotte and Lilljebjörn, Henrik and Fioretos, Thoas and Fontes, Magnus},
  issn         = {1471-2105},
  language     = {eng},
  number       = {191},
  pages        = {1--20},
  publisher    = {BioMed Central},
  series       = {BMC Bioinformatics},
  title        = {Integrative analysis of gene expression and copy number alterations using canonical correlation analysis},
  url          = {http://dx.doi.org/10.1186/1471-2105-11-191},
  volume       = {11},
  year         = {2010},
}