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Component retention in principal component analysis with application to cDNA microarray data

Shannon entropy is used to provide an estimate of the number of interpretable components in a principal component analysis. In addition, several ad hoc stopping rules for dimension determination are reviewed and a modification of the broken stick model is presented. The modification incorporates a t...

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Detalles Bibliográficos
Autores principales: Cangelosi, Richard, Goriely, Alain
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1797006/
https://www.ncbi.nlm.nih.gov/pubmed/17229320
http://dx.doi.org/10.1186/1745-6150-2-2
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author Cangelosi, Richard
Goriely, Alain
author_facet Cangelosi, Richard
Goriely, Alain
author_sort Cangelosi, Richard
collection PubMed
description Shannon entropy is used to provide an estimate of the number of interpretable components in a principal component analysis. In addition, several ad hoc stopping rules for dimension determination are reviewed and a modification of the broken stick model is presented. The modification incorporates a test for the presence of an "effective degeneracy" among the subspaces spanned by the eigenvectors of the correlation matrix of the data set then allocates the total variance among subspaces. A summary of the performance of the methods applied to both published microarray data sets and to simulated data is given. This article was reviewed by Orly Alter, John Spouge (nominated by Eugene Koonin), David Horn and Roy Varshavsky (both nominated by O. Alter).
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spelling pubmed-17970062007-02-16 Component retention in principal component analysis with application to cDNA microarray data Cangelosi, Richard Goriely, Alain Biol Direct Research Shannon entropy is used to provide an estimate of the number of interpretable components in a principal component analysis. In addition, several ad hoc stopping rules for dimension determination are reviewed and a modification of the broken stick model is presented. The modification incorporates a test for the presence of an "effective degeneracy" among the subspaces spanned by the eigenvectors of the correlation matrix of the data set then allocates the total variance among subspaces. A summary of the performance of the methods applied to both published microarray data sets and to simulated data is given. This article was reviewed by Orly Alter, John Spouge (nominated by Eugene Koonin), David Horn and Roy Varshavsky (both nominated by O. Alter). BioMed Central 2007-01-17 /pmc/articles/PMC1797006/ /pubmed/17229320 http://dx.doi.org/10.1186/1745-6150-2-2 Text en Copyright © 2007 Cangelosi and Goriely; 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
Cangelosi, Richard
Goriely, Alain
Component retention in principal component analysis with application to cDNA microarray data
title Component retention in principal component analysis with application to cDNA microarray data
title_full Component retention in principal component analysis with application to cDNA microarray data
title_fullStr Component retention in principal component analysis with application to cDNA microarray data
title_full_unstemmed Component retention in principal component analysis with application to cDNA microarray data
title_short Component retention in principal component analysis with application to cDNA microarray data
title_sort component retention in principal component analysis with application to cdna microarray data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1797006/
https://www.ncbi.nlm.nih.gov/pubmed/17229320
http://dx.doi.org/10.1186/1745-6150-2-2
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