Evidence map›Paper›PMID 28950376›Full record

ArticleSystematic biology2018

Phylogenetic Factor Analysis.

Max R Tolkoff, Michael E Alfaro, Guy Baele, Philippe Lemey, Marc A Suchard

Abstract read
In one paragraph

Article in Systematic biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Leveraging graphical model techniques to study evolution on phylogenetic networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
    Review
  4. Article
  5. Article
  6. Data integration in Bayesian phylogenetics.Annual review of statistics and its application · 2023
    Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Max R TolkoffDepartment of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California, 650 Charles E. Young Dr. South Los Angeles, CA 90095-1772, USA.
Michael E AlfaroDepartment of Ecology and Evolutionary Biology, University of California, 610 Charles E. Young Drive South Los Angeles, CA 90095-1606, USA.
Guy BaeleDepartment of Microbiology and Immunology, Rega Institute, KU Leuven, Minderbroederstraat 10 BE-3000 Leuven, Belgium.
Philippe LemeyDepartment of Microbiology and Immunology, Rega Institute, KU Leuven, Minderbroederstraat 10 BE-3000 Leuven, Belgium.
Marc A SuchardDepartment of Biostatistics, Jonathan and Karin Fielding School of Public Health, University of California, 650 Charles E. Young Dr. South Los Angeles, CA 90095-1772, USA.

Funding

Genomics, GPUs, and Next Generation Computational StatisticsR01HG006139 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SOBEL, ERIC · 2011 to 2023
$5.1M
Tracking Evolution and Spread of Viral Genomes by Geospatial Observation ErrorR01AI117011 · NIAID · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI GONZALEZ HERNANDEZ, GRACIELA, SCOTCH, MATTHEW · 2016 to 2019
$1.9M
BAYESIAN MODELING AND DATA INTEGRATION IN INFECTIOUS DISEASE PHYLODYNAMICSR01AI107034 · NIAID · UNIVERSITY OF WASHINGTON · PI MININ, VOLODYMYR · 2013 to 2017
$1.7M
NHGRI NIH HHS R01 HG006139NIAID NIH HHS R01 AI107034NIAID NIH HHS R01 AI117011
6 · The paper itself

Abstract

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.

Indexed as

Models, GeneticPhylogenyAnimalsClassificationComputer SimulationFactor Analysis, Statistical

Identifiers

PMID28950376
PMCPMC5920329

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.