schliessen

Filtern

 

Bibliotheken

Phylogenetic Factor Analysis

Phylogenetic comparative methods explore the relationships between quantitative traits adjusting for shared evolutionary history. This adjustment often occurs through a Brownian diffusion process along the branches of the phylogeny that generates model residuals or the traits themselves. For high-di... Full description

Journal Title: Systematic biology 2018, Vol.67 (3), p.384-399
Main Author: Tolkoff, Max R
Other Authors: Alfaro, Michael E , Baele, Guy , Lemey, Philippe , Suchard, Marc A
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: England: Oxford University Press
ID: ISSN: 1063-5157
Link: https://www.ncbi.nlm.nih.gov/pubmed/28950376
Zum Text:
SendSend as email Add to Book BagAdd to Book Bag
Staff View
recordid: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5920329
title: Phylogenetic Factor Analysis
format: Article
creator:
  • Tolkoff, Max R
  • Alfaro, Michael E
  • Baele, Guy
  • Lemey, Philippe
  • Suchard, Marc A
subjects:
  • Animals
  • Applications
  • Bayesian inference
  • Classification - methods
  • comparative methods
  • Computation
  • Computer Simulation
  • Factor Analysis, Statistical
  • Methodology
  • Models, Genetic
  • morphometrics
  • phylogenetics
  • Phylogeny
  • Regular
  • REGULAR ARTICLES
  • Statistics
ispartof: Systematic biology, 2018, Vol.67 (3), p.384-399
description: Phylogenetic comparative methods explore the relationships between quantitative traits adjusting for shared evolutionary history. This adjustment often occurs through a Brownian diffusion process along the branches of the phylogeny that generates model residuals or the traits themselves. For high-dimensional traits, inferring all pair-wise correlations within the multivariate diffusion is limiting. To circumvent this problem, we propose phylogenetic factor analysis (PFA) that assumes a small unknown number of independent evolutionary factors arise along the phylogeny and these factors generate clusters of dependent traits. Set in a Bayesian framework, PFA provides measures of uncertainty on the factor number and groupings, combines both continuous and discrete traits, integrates over missing measurements and incorporates phylogenetic uncertainty with the help of molecular sequences. We develop Gibbs samplers based on dynamic programming to estimate the PFA posterior distribution, over 3-fold faster than for multivariate diffusion and a further order-of-magnitude more efficiently in the presence of latent traits. We further propose a novel marginal likelihood estimator for previously impractical models with discrete data and find that PFA also provides a better fit than multivariate diffusion in evolutionary questions in columbine flower development, placental reproduction transitions and triggerfish fin morphometry.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 1063-5157
fulltext: fulltext
issn:
  • 1063-5157
  • 1076-836X
url: Link


@attributes
NO1
SEARCH_ENGINEprimo_central_multiple_fe
SEARCH_ENGINE_TYPEPrimo Central Search Engine
RANK2.5569615
LOCALfalse
PrimoNMBib
record
control
sourceidjstor_pubme
recordidTN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5920329
sourceformatXML
sourcesystemPC
jstor_id26581965
oup_id10.1093/sysbio/syx066
sourcerecordid26581965
originalsourceidFETCH-LOGICAL-1522t-a65dc9d1344e6c677dbf7ff1a928d9fce5250f077d339c2c0d6d3b85c79687d23
addsrcrecordideNqFkclLxDAYxYMoOi5HbyoevVSzNElzEQZxg0E9KHgLaZaZSKcZm1ad_94MHVcQTwnJy-97Lw-AXQSPERTkJM5j6UNa3iBjK2CAIGdZQdjj6mLPSEYR5RtgM8YnCBFiFK2DDVwICglnA7B3N5lXYWxr23p9eKF0G5rDYa2qefRxG6w5VUW7s1y3wMPF-f3ZVTa6vbw-G44yRDFuM8Wo0cIgkueWaca5KR13DimBCyOcthRT6GA6J0RorKFhhpQF1VywghtMtsBpz5115dQabeu2UZWcNX6qmrkMysufN7WfyHF4kVRgSLBIgJseEGa2Vr6xP96aFE4aa7qZfHUy_YI02mlOCcHJJbfQlYWhDkKMiSgoLnkCHi0dNeG5s7GVUx-1rSpV29BFiUROWC4oWszOeqluQoyNdZ-zEZSLimRfkewrSvqD72E_1R-dJAH5BdS-Va0Pi-y--hO7dBxSzP8c7PfSp5ja_jLAaIEEo-QdXRy4uw
sourcetypeOpen Access Repository
isCDItrue
recordtypearticle
pqid1943649519
display
typearticle
titlePhylogenetic Factor Analysis
sourceAlma/SFX Local Collection
creatorTolkoff, Max R ; Alfaro, Michael E ; Baele, Guy ; Lemey, Philippe ; Suchard, Marc A
contributorKubatko, Laura
creatorcontribTolkoff, Max R ; Alfaro, Michael E ; Baele, Guy ; Lemey, Philippe ; Suchard, Marc A ; Kubatko, Laura
descriptionPhylogenetic comparative methods explore the relationships between quantitative traits adjusting for shared evolutionary history. This adjustment often occurs through a Brownian diffusion process along the branches of the phylogeny that generates model residuals or the traits themselves. For high-dimensional traits, inferring all pair-wise correlations within the multivariate diffusion is limiting. To circumvent this problem, we propose phylogenetic factor analysis (PFA) that assumes a small unknown number of independent evolutionary factors arise along the phylogeny and these factors generate clusters of dependent traits. Set in a Bayesian framework, PFA provides measures of uncertainty on the factor number and groupings, combines both continuous and discrete traits, integrates over missing measurements and incorporates phylogenetic uncertainty with the help of molecular sequences. We develop Gibbs samplers based on dynamic programming to estimate the PFA posterior distribution, over 3-fold faster than for multivariate diffusion and a further order-of-magnitude more efficiently in the presence of latent traits. We further propose a novel marginal likelihood estimator for previously impractical models with discrete data and find that PFA also provides a better fit than multivariate diffusion in evolutionary questions in columbine flower development, placental reproduction transitions and triggerfish fin morphometry.
identifier
0ISSN: 1063-5157
1EISSN: 1076-836X
2DOI: 10.1093/sysbio/syx066
3PMID: 28950376
languageeng
publisherEngland: Oxford University Press
subjectAnimals ; Applications ; Bayesian inference ; Classification - methods ; comparative methods ; Computation ; Computer Simulation ; Factor Analysis, Statistical ; Methodology ; Models, Genetic ; morphometrics ; phylogenetics ; Phylogeny ; Regular ; REGULAR ARTICLES ; Statistics
ispartofSystematic biology, 2018, Vol.67 (3), p.384-399
rights
0The Author(s) 2017
1The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2017
2The Author(s) 2017. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: 2017
lds50peer_reviewed
oafree_for_read
citedbyFETCH-LOGICAL-1522t-a65dc9d1344e6c677dbf7ff1a928d9fce5250f077d339c2c0d6d3b85c79687d23
citesFETCH-LOGICAL-1522t-a65dc9d1344e6c677dbf7ff1a928d9fce5250f077d339c2c0d6d3b85c79687d23
links
openurl$$Topenurl_article
openurlfulltext$$Topenurlfull_article
thumbnail$$Usyndetics_thumb_exl
backlink$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28950376$$D View this record in MEDLINE/PubMed
search
contributorKubatko, Laura
creatorcontrib
0Tolkoff, Max R
1Alfaro, Michael E
2Baele, Guy
3Lemey, Philippe
4Suchard, Marc A
title
0Phylogenetic Factor Analysis
1Systematic biology
addtitleSyst Biol
descriptionPhylogenetic comparative methods explore the relationships between quantitative traits adjusting for shared evolutionary history. This adjustment often occurs through a Brownian diffusion process along the branches of the phylogeny that generates model residuals or the traits themselves. For high-dimensional traits, inferring all pair-wise correlations within the multivariate diffusion is limiting. To circumvent this problem, we propose phylogenetic factor analysis (PFA) that assumes a small unknown number of independent evolutionary factors arise along the phylogeny and these factors generate clusters of dependent traits. Set in a Bayesian framework, PFA provides measures of uncertainty on the factor number and groupings, combines both continuous and discrete traits, integrates over missing measurements and incorporates phylogenetic uncertainty with the help of molecular sequences. We develop Gibbs samplers based on dynamic programming to estimate the PFA posterior distribution, over 3-fold faster than for multivariate diffusion and a further order-of-magnitude more efficiently in the presence of latent traits. We further propose a novel marginal likelihood estimator for previously impractical models with discrete data and find that PFA also provides a better fit than multivariate diffusion in evolutionary questions in columbine flower development, placental reproduction transitions and triggerfish fin morphometry.
subject
0Animals
1Applications
2Bayesian inference
3Classification - methods
4comparative methods
5Computation
6Computer Simulation
7Factor Analysis, Statistical
8Methodology
9Models, Genetic
10morphometrics
11phylogenetics
12Phylogeny
13Regular
14REGULAR ARTICLES
15Statistics
issn
01063-5157
11076-836X
fulltexttrue
rsrctypearticle
creationdate2018
recordtypearticle
recordideNqFkclLxDAYxYMoOi5HbyoevVSzNElzEQZxg0E9KHgLaZaZSKcZm1ad_94MHVcQTwnJy-97Lw-AXQSPERTkJM5j6UNa3iBjK2CAIGdZQdjj6mLPSEYR5RtgM8YnCBFiFK2DDVwICglnA7B3N5lXYWxr23p9eKF0G5rDYa2qefRxG6w5VUW7s1y3wMPF-f3ZVTa6vbw-G44yRDFuM8Wo0cIgkueWaca5KR13DimBCyOcthRT6GA6J0RorKFhhpQF1VywghtMtsBpz5115dQabeu2UZWcNX6qmrkMysufN7WfyHF4kVRgSLBIgJseEGa2Vr6xP96aFE4aa7qZfHUy_YI02mlOCcHJJbfQlYWhDkKMiSgoLnkCHi0dNeG5s7GVUx-1rSpV29BFiUROWC4oWszOeqluQoyNdZ-zEZSLimRfkewrSvqD72E_1R-dJAH5BdS-Va0Pi-y--hO7dBxSzP8c7PfSp5ja_jLAaIEEo-QdXRy4uw
startdate20180501
enddate20180501
creator
0Tolkoff, Max R
1Alfaro, Michael E
2Baele, Guy
3Lemey, Philippe
4Suchard, Marc A
general
0Oxford University Press
1Society of Systematic Biologists
scope
0CGR
1CUY
2CVF
3ECM
4EIF
5NPM
6AAYXX
7CITATION
87X8
9BOBZL
10CLFQK
115PM
sort
creationdate20180501
titlePhylogenetic Factor Analysis
authorTolkoff, Max R ; Alfaro, Michael E ; Baele, Guy ; Lemey, Philippe ; Suchard, Marc A
facets
frbrtype5
frbrgroupidcdi_FETCH-LOGICAL-1522t-a65dc9d1344e6c677dbf7ff1a928d9fce5250f077d339c2c0d6d3b85c79687d23
rsrctypearticles
prefilterarticles
languageeng
creationdate2018
topic
0Animals
1Applications
2Bayesian inference
3Classification - methods
4comparative methods
5Computation
6Computer Simulation
7Factor Analysis, Statistical
8Methodology
9Models, Genetic
10morphometrics
11phylogenetics
12Phylogeny
13Regular
14REGULAR ARTICLES
15Statistics
toplevel
0peer_reviewed
1online_resources
creatorcontrib
0Tolkoff, Max R
1Alfaro, Michael E
2Baele, Guy
3Lemey, Philippe
4Suchard, Marc A
collection
0Medline
1MEDLINE
2MEDLINE (Ovid)
3MEDLINE
4MEDLINE
5PubMed
6CrossRef
7MEDLINE - Academic
8OpenAIRE (Open Access)
9OpenAIRE
10PubMed Central (Full Participant titles)
jtitleSystematic biology
delivery
delcategoryRemote Search Resource
fulltextfulltext
addata
au
0Tolkoff, Max R
1Alfaro, Michael E
2Baele, Guy
3Lemey, Philippe
4Suchard, Marc A
formatjournal
genrearticle
ristypeJOUR
atitlePhylogenetic Factor Analysis
jtitleSystematic biology
addtitleSyst Biol
date2018-05-01
risdate2018
volume67
issue3
spage384
epage399
pages384-399
issn1063-5157
eissn1076-836X
abstractPhylogenetic comparative methods explore the relationships between quantitative traits adjusting for shared evolutionary history. This adjustment often occurs through a Brownian diffusion process along the branches of the phylogeny that generates model residuals or the traits themselves. For high-dimensional traits, inferring all pair-wise correlations within the multivariate diffusion is limiting. To circumvent this problem, we propose phylogenetic factor analysis (PFA) that assumes a small unknown number of independent evolutionary factors arise along the phylogeny and these factors generate clusters of dependent traits. Set in a Bayesian framework, PFA provides measures of uncertainty on the factor number and groupings, combines both continuous and discrete traits, integrates over missing measurements and incorporates phylogenetic uncertainty with the help of molecular sequences. We develop Gibbs samplers based on dynamic programming to estimate the PFA posterior distribution, over 3-fold faster than for multivariate diffusion and a further order-of-magnitude more efficiently in the presence of latent traits. We further propose a novel marginal likelihood estimator for previously impractical models with discrete data and find that PFA also provides a better fit than multivariate diffusion in evolutionary questions in columbine flower development, placental reproduction transitions and triggerfish fin morphometry.
copEngland
pubOxford University Press
pmid28950376
doi10.1093/sysbio/syx066
oafree_for_read