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Probabilistic principal component analysis for phylogenetic comparative studies.

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Title: Probabilistic principal component analysis for phylogenetic comparative studies.
Authors: Caetano, Daniel S1 (AUTHOR), Hearn, David J1 (AUTHOR)
Source: Evolution. May2026, Vol. 80 Issue 5, p902-911. 10p.
Subjects: Principal components analysis, Evolutionary models, Phylogeny, Biometry, Dimensional reduction algorithms
Abstract: Principal component analysis (PCA) is one of the most widely used approaches for multivariate datasets. Biologists use PCA to visualize data, identify patterns in large datasets, determine independent axes of variation, and reduce dimensionality for further statistical analyses. Phylogenetic PCA is an extension of regular PCA that seeks to identify the major axes of variation independent of the phylogeny. We extend these methods by estimating PCA parameters using an explicit probability modeling framework. We implement multiple models of trait evolution (Brownian motion, Ornstein-Uhlenbeck, Early Burst, and Pagel's λ) and use the Akaike information criterion for model selection. We also introduce a probabilistic approach to select the number of principal components to retain from a PCA. We demonstrate the advantages of probabilistic PCA, such as incorporating the error, or noise, arising from dimensionality reduction, which is ignored in regular PCA. We use extensive simulations and an empirical dataset with 35 traits to show the method's performance. We implemented the new approach in the R package "do3PCA" available from the RCran repository. [ABSTRACT FROM AUTHOR]
Copyright of Evolution is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 194211872
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Probabilistic principal component analysis for phylogenetic comparative studies.
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  Data: <searchLink fieldCode="AR" term="%22Caetano%2C+Daniel+S%22">Caetano, Daniel S</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hearn%2C+David+J%22">Hearn, David J</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Evolution%22">Evolution</searchLink>. May2026, Vol. 80 Issue 5, p902-911. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+models%22">Evolutionary models</searchLink><br /><searchLink fieldCode="DE" term="%22Phylogeny%22">Phylogeny</searchLink><br /><searchLink fieldCode="DE" term="%22Biometry%22">Biometry</searchLink><br /><searchLink fieldCode="DE" term="%22Dimensional+reduction+algorithms%22">Dimensional reduction algorithms</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Principal component analysis (PCA) is one of the most widely used approaches for multivariate datasets. Biologists use PCA to visualize data, identify patterns in large datasets, determine independent axes of variation, and reduce dimensionality for further statistical analyses. Phylogenetic PCA is an extension of regular PCA that seeks to identify the major axes of variation independent of the phylogeny. We extend these methods by estimating PCA parameters using an explicit probability modeling framework. We implement multiple models of trait evolution (Brownian motion, Ornstein-Uhlenbeck, Early Burst, and Pagel's λ) and use the Akaike information criterion for model selection. We also introduce a probabilistic approach to select the number of principal components to retain from a PCA. We demonstrate the advantages of probabilistic PCA, such as incorporating the error, or noise, arising from dimensionality reduction, which is ignored in regular PCA. We use extensive simulations and an empirical dataset with 35 traits to show the method's performance. We implemented the new approach in the R package "do3PCA" available from the RCran repository. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Evolution is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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    Identifiers:
      – Type: doi
        Value: 10.1093/evolut/qpag044
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 10
        StartPage: 902
    Subjects:
      – SubjectFull: Principal components analysis
        Type: general
      – SubjectFull: Evolutionary models
        Type: general
      – SubjectFull: Phylogeny
        Type: general
      – SubjectFull: Biometry
        Type: general
      – SubjectFull: Dimensional reduction algorithms
        Type: general
    Titles:
      – TitleFull: Probabilistic principal component analysis for phylogenetic comparative studies.
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            NameFull: Caetano, Daniel S
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            NameFull: Hearn, David J
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 80
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              Value: 5
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            – TitleFull: Evolution
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