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Joint mapping of genes and conditions via multidimensional unfolding analysis

BACKGROUND: Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions based on their similarity in overall expression over profiles but also to gain information on more subtle relations between g...

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Autores principales: Van Deun, Katrijn, Marchal, Kathleen, Heiser, Willem J, Engelen, Kristof, Van Mechelen, Iven
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1904247/
https://www.ncbi.nlm.nih.gov/pubmed/17550582
http://dx.doi.org/10.1186/1471-2105-8-181
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author Van Deun, Katrijn
Marchal, Kathleen
Heiser, Willem J
Engelen, Kristof
Van Mechelen, Iven
author_facet Van Deun, Katrijn
Marchal, Kathleen
Heiser, Willem J
Engelen, Kristof
Van Mechelen, Iven
author_sort Van Deun, Katrijn
collection PubMed
description BACKGROUND: Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions based on their similarity in overall expression over profiles but also to gain information on more subtle relations between genes and conditions. Getting a clear visual overview of all these patterns in a single easy-to-grasp representation is a useful preliminary analysis step: We propose to use for this purpose an advanced exploratory method, called multidimensional unfolding. RESULTS: We present a novel algorithm for multidimensional unfolding that overcomes both general problems and problems that are specific for the analysis of gene expression data sets. Applying the algorithm to two publicly available microarray compendia illustrates its power as a tool for exploratory data analysis: The unfolding analysis of a first data set resulted in a two-dimensional representation which clearly reveals temporal regulation patterns for the genes and a meaningful structure for the time points, while the analysis of a second data set showed the algorithm's ability to go beyond a mere identification of those genes that discriminate between different patient or tissue types. CONCLUSION: Multidimensional unfolding offers a useful tool for preliminary explorations of microarray data: By relying on an easy-to-grasp low-dimensional geometric framework, relations among genes, among conditions and between genes and conditions are simultaneously represented in an accessible way which may reveal interesting patterns in the data. An additional advantage of the method is that it can be applied to the raw data without necessitating the choice of suitable genewise transformations of the data.
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spelling pubmed-19042472007-06-29 Joint mapping of genes and conditions via multidimensional unfolding analysis Van Deun, Katrijn Marchal, Kathleen Heiser, Willem J Engelen, Kristof Van Mechelen, Iven BMC Bioinformatics Methodology Article BACKGROUND: Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions based on their similarity in overall expression over profiles but also to gain information on more subtle relations between genes and conditions. Getting a clear visual overview of all these patterns in a single easy-to-grasp representation is a useful preliminary analysis step: We propose to use for this purpose an advanced exploratory method, called multidimensional unfolding. RESULTS: We present a novel algorithm for multidimensional unfolding that overcomes both general problems and problems that are specific for the analysis of gene expression data sets. Applying the algorithm to two publicly available microarray compendia illustrates its power as a tool for exploratory data analysis: The unfolding analysis of a first data set resulted in a two-dimensional representation which clearly reveals temporal regulation patterns for the genes and a meaningful structure for the time points, while the analysis of a second data set showed the algorithm's ability to go beyond a mere identification of those genes that discriminate between different patient or tissue types. CONCLUSION: Multidimensional unfolding offers a useful tool for preliminary explorations of microarray data: By relying on an easy-to-grasp low-dimensional geometric framework, relations among genes, among conditions and between genes and conditions are simultaneously represented in an accessible way which may reveal interesting patterns in the data. An additional advantage of the method is that it can be applied to the raw data without necessitating the choice of suitable genewise transformations of the data. BioMed Central 2007-06-05 /pmc/articles/PMC1904247/ /pubmed/17550582 http://dx.doi.org/10.1186/1471-2105-8-181 Text en Copyright © 2007 Van Deun 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
Van Deun, Katrijn
Marchal, Kathleen
Heiser, Willem J
Engelen, Kristof
Van Mechelen, Iven
Joint mapping of genes and conditions via multidimensional unfolding analysis
title Joint mapping of genes and conditions via multidimensional unfolding analysis
title_full Joint mapping of genes and conditions via multidimensional unfolding analysis
title_fullStr Joint mapping of genes and conditions via multidimensional unfolding analysis
title_full_unstemmed Joint mapping of genes and conditions via multidimensional unfolding analysis
title_short Joint mapping of genes and conditions via multidimensional unfolding analysis
title_sort joint mapping of genes and conditions via multidimensional unfolding analysis
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1904247/
https://www.ncbi.nlm.nih.gov/pubmed/17550582
http://dx.doi.org/10.1186/1471-2105-8-181
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