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Integrated analysis of DNA copy number and gene expression microarray data using gene sets

BACKGROUND: Genes that play an important role in tumorigenesis are expected to show association between DNA copy number and RNA expression. Optimal power to find such associations can only be achieved if analysing copy number and gene expression jointly. Furthermore, some copy number changes extend...

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Autores principales: Menezes, Renée X, Boetzer, Marten, Sieswerda, Melle, van Ommen, Gert-Jan B, Boer, Judith M
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2753845/
https://www.ncbi.nlm.nih.gov/pubmed/19563656
http://dx.doi.org/10.1186/1471-2105-10-203
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author Menezes, Renée X
Boetzer, Marten
Sieswerda, Melle
van Ommen, Gert-Jan B
Boer, Judith M
author_facet Menezes, Renée X
Boetzer, Marten
Sieswerda, Melle
van Ommen, Gert-Jan B
Boer, Judith M
author_sort Menezes, Renée X
collection PubMed
description BACKGROUND: Genes that play an important role in tumorigenesis are expected to show association between DNA copy number and RNA expression. Optimal power to find such associations can only be achieved if analysing copy number and gene expression jointly. Furthermore, some copy number changes extend over larger chromosomal regions affecting the expression levels of multiple resident genes. RESULTS: We propose to analyse copy number and expression array data using gene sets, rather than individual genes. The proposed model is robust and sensitive. We re-analysed two publicly available datasets as illustration. These two independent breast cancer datasets yielded similar patterns of association between gene dosage and gene expression levels, in spite of different platforms having been used. Our comparisons show a clear advantage to using sets of genes' expressions to detect associations with long-spanning, low-amplitude copy number aberrations. In addition, our model allows for using additional explanatory variables and does not require mapping between copy number and expression probes. CONCLUSION: We developed a general and flexible tool for integration of multiple microarray data sets, and showed how the identification of genes whose expression is affected by copy number aberrations provides a powerful approach to prioritize putative targets for functional validation.
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spelling pubmed-27538452009-09-30 Integrated analysis of DNA copy number and gene expression microarray data using gene sets Menezes, Renée X Boetzer, Marten Sieswerda, Melle van Ommen, Gert-Jan B Boer, Judith M BMC Bioinformatics Research Article BACKGROUND: Genes that play an important role in tumorigenesis are expected to show association between DNA copy number and RNA expression. Optimal power to find such associations can only be achieved if analysing copy number and gene expression jointly. Furthermore, some copy number changes extend over larger chromosomal regions affecting the expression levels of multiple resident genes. RESULTS: We propose to analyse copy number and expression array data using gene sets, rather than individual genes. The proposed model is robust and sensitive. We re-analysed two publicly available datasets as illustration. These two independent breast cancer datasets yielded similar patterns of association between gene dosage and gene expression levels, in spite of different platforms having been used. Our comparisons show a clear advantage to using sets of genes' expressions to detect associations with long-spanning, low-amplitude copy number aberrations. In addition, our model allows for using additional explanatory variables and does not require mapping between copy number and expression probes. CONCLUSION: We developed a general and flexible tool for integration of multiple microarray data sets, and showed how the identification of genes whose expression is affected by copy number aberrations provides a powerful approach to prioritize putative targets for functional validation. BioMed Central 2009-06-29 /pmc/articles/PMC2753845/ /pubmed/19563656 http://dx.doi.org/10.1186/1471-2105-10-203 Text en Copyright © 2009 Menezes 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
Menezes, Renée X
Boetzer, Marten
Sieswerda, Melle
van Ommen, Gert-Jan B
Boer, Judith M
Integrated analysis of DNA copy number and gene expression microarray data using gene sets
title Integrated analysis of DNA copy number and gene expression microarray data using gene sets
title_full Integrated analysis of DNA copy number and gene expression microarray data using gene sets
title_fullStr Integrated analysis of DNA copy number and gene expression microarray data using gene sets
title_full_unstemmed Integrated analysis of DNA copy number and gene expression microarray data using gene sets
title_short Integrated analysis of DNA copy number and gene expression microarray data using gene sets
title_sort integrated analysis of dna copy number and gene expression microarray data using gene sets
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2753845/
https://www.ncbi.nlm.nih.gov/pubmed/19563656
http://dx.doi.org/10.1186/1471-2105-10-203
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