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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...
Autores principales: | , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2007
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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). |
format | Text |
id | pubmed-1797006 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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