ANOVA-principal component analysis and ANOVA-simultaneous component analysis: a comparison
ANOVA–simultaneous component analysis (ASCA) is a recently developed tool to analyze multivariate data. In this paper, we enhance the explorative capability of ASCA by introducing a projection of the observations on the principal component subspace to visualize the variation among the measurements....
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| Veröffentlicht in: | Journal of chemometrics 2011-10, Vol.25 (10), p.561-567 |
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| container_title | Journal of chemometrics |
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| creator | Zwanenburg, Gooitzen Hoefsloot, Huub C.J. Westerhuis, Johan A. Jansen, Jeroen J. Smilde, Age K. |
| description | ANOVA–simultaneous component analysis (ASCA) is a recently developed tool to analyze multivariate data. In this paper, we enhance the explorative capability of ASCA by introducing a projection of the observations on the principal component subspace to visualize the variation among the measurements. We compare the significance of experimental effects for ASCA and ANOVA–principal component analysis (PCA), a similar tool to explore multivariate data, by using permutation tests. Furthermore, we quantify the quality of the loadings estimate obtained with ASCA and compare this with the loadings estimate obtained with ANOVA–PCA. Copyright © 2011 John Wiley & Sons, Ltd.
This work presents an enhancement of ANOVA‐simultaneous component analysis by projecting the observations onto the principal component subspace, thus allowing the visualization of the variation of the measurements. Furthermore, using a synthetic data set, a comparison is made between the significance of experimental effects and the quality of estimated loadings for ANOVA‐simultaneous component analysis and ANOVA‐principal component analysis. |
| doi_str_mv | 10.1002/cem.1400 |
| format | Article |
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This work presents an enhancement of ANOVA‐simultaneous component analysis by projecting the observations onto the principal component subspace, thus allowing the visualization of the variation of the measurements. Furthermore, using a synthetic data set, a comparison is made between the significance of experimental effects and the quality of estimated loadings for ANOVA‐simultaneous component analysis and ANOVA‐principal component analysis.</description><identifier>ISSN: 0886-9383</identifier><identifier>ISSN: 1099-128X</identifier><identifier>EISSN: 1099-128X</identifier><identifier>DOI: 10.1002/cem.1400</identifier><identifier>CODEN: JOCHEU</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>ANOVA-PCA ; ASCA ; Chemistry ; Chemometrics ; Comparative analysis ; Data processing ; Estimates ; Exact sciences and technology ; General and physical chemistry ; General. Nomenclature, chemical documentation, computer chemistry ; Measurement ; metabolomics ; multivariate data analysis ; permutation tests ; Permutations ; Principal components analysis ; Projection ; Proteomics ; Subspaces ; Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry ; Variance analysis</subject><ispartof>Journal of chemometrics, 2011-10, Vol.25 (10), p.561-567</ispartof><rights>Copyright © 2011 John Wiley & Sons, Ltd.</rights><rights>2015 INIST-CNRS</rights><rights>Copyright John Wiley and Sons, Limited Oct 2011</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4260-1c8d932b6f91f29d261db03439d22e54057ff59f840dcd3f6264f1baa0f1eb803</citedby><cites>FETCH-LOGICAL-c4260-1c8d932b6f91f29d261db03439d22e54057ff59f840dcd3f6264f1baa0f1eb803</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fcem.1400$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fcem.1400$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,27922,27923,45572,45573</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24686594$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Zwanenburg, Gooitzen</creatorcontrib><creatorcontrib>Hoefsloot, Huub C.J.</creatorcontrib><creatorcontrib>Westerhuis, Johan A.</creatorcontrib><creatorcontrib>Jansen, Jeroen J.</creatorcontrib><creatorcontrib>Smilde, Age K.</creatorcontrib><title>ANOVA-principal component analysis and ANOVA-simultaneous component analysis: a comparison</title><title>Journal of chemometrics</title><addtitle>J. Chemometrics</addtitle><description>ANOVA–simultaneous component analysis (ASCA) is a recently developed tool to analyze multivariate data. In this paper, we enhance the explorative capability of ASCA by introducing a projection of the observations on the principal component subspace to visualize the variation among the measurements. We compare the significance of experimental effects for ASCA and ANOVA–principal component analysis (PCA), a similar tool to explore multivariate data, by using permutation tests. Furthermore, we quantify the quality of the loadings estimate obtained with ASCA and compare this with the loadings estimate obtained with ANOVA–PCA. Copyright © 2011 John Wiley & Sons, Ltd.
This work presents an enhancement of ANOVA‐simultaneous component analysis by projecting the observations onto the principal component subspace, thus allowing the visualization of the variation of the measurements. Furthermore, using a synthetic data set, a comparison is made between the significance of experimental effects and the quality of estimated loadings for ANOVA‐simultaneous component analysis and ANOVA‐principal component analysis.</description><subject>ANOVA-PCA</subject><subject>ASCA</subject><subject>Chemistry</subject><subject>Chemometrics</subject><subject>Comparative analysis</subject><subject>Data processing</subject><subject>Estimates</subject><subject>Exact sciences and technology</subject><subject>General and physical chemistry</subject><subject>General. Nomenclature, chemical documentation, computer chemistry</subject><subject>Measurement</subject><subject>metabolomics</subject><subject>multivariate data analysis</subject><subject>permutation tests</subject><subject>Permutations</subject><subject>Principal components analysis</subject><subject>Projection</subject><subject>Proteomics</subject><subject>Subspaces</subject><subject>Theory of reactions, general kinetics. Catalysis. 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This work presents an enhancement of ANOVA‐simultaneous component analysis by projecting the observations onto the principal component subspace, thus allowing the visualization of the variation of the measurements. Furthermore, using a synthetic data set, a comparison is made between the significance of experimental effects and the quality of estimated loadings for ANOVA‐simultaneous component analysis and ANOVA‐principal component analysis.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/cem.1400</doi><tpages>7</tpages></addata></record> |
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| subjects | ANOVA-PCA ASCA Chemistry Chemometrics Comparative analysis Data processing Estimates Exact sciences and technology General and physical chemistry General. Nomenclature, chemical documentation, computer chemistry Measurement metabolomics multivariate data analysis permutation tests Permutations Principal components analysis Projection Proteomics Subspaces Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry Variance analysis |
| title | ANOVA-principal component analysis and ANOVA-simultaneous component analysis: a comparison |
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