Modelling and Analysing Multivariate Trait Evolution using R

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Course Modelling and Analysing Multivariate Traits Evolution on Phylogenetic Trees using mvMorph

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Course Overview

On this course, participants will be introduced to multivariate phylogenetic comparative methods with the mvMORPH R package.

The mvMORPH package contains tools for modelling the evolution of correlated continuous traits (e.g. morphometric measurement, geometric morphometric datasets, life history traits, gene expression data, etc.) on phylogenetic trees [with either fossil species, extant species or both] as well as statistical tools such as multivariate generalized least squares (GLS) linear models -e.g. multivariate regression, MANOVA, MANCOVA – for studying comparative datasets.

During the course, participants will first be introduced to some theory with illustrative examples (both from simulated data as well as students’ own datasets) and will then learn how to interpret the models, their parameters, as well as how to assess their reliability.

We would like to encourage participants to bring along their own dataset with a matching phylogenetic tree (or sample of trees) to analyse in between the live sessions and discuss with the instructor.

Places are limited to 16 participants.

Programme

Introduction to phylogenies, trait evolution and the comparative methods
- Short introduction on the modelling rationale and theoretical basis on trait (multivariate) evolution and models
- Illustration with simulation examples in R (e.g. 3D plot of bivariate processes)
Modelling the evolution of traits on trees (simulations, model fit and comparison)
- Review of some multivariate models (BM, OU, EB, Shift…), assumptions, and limits.
- Step by step procedure for model comparison and interpretation of parameters using simulated and empirical datasets.
- Hypothesis testing and constrained parameters estimation
Working with high-dimensional datasets
- Introduction to the high-dimensional challenges (when the number of traits approach or is larger than the number of species such as in geometric morphometric and gene expression data) of comparative methods (e.g. comparison of likelihood, penalized likelihood, and alternative techniques)
- Model fit on high-dimensional datasets (model comparison, estimation of parameters, reconstruction of evolutionary trajectories)
Fitting linear models (MANOVA, MANCOVA, multivariate regression) to comparative data
- Introduction to phylogenetic linear models and multivariate counterparts, their assumptions, the various tests.
- Linear hypothesis testing
- Illustration on both empirical and simulated datasets
Using diagnostic plots, simulations, and Monte-Carlo techniques to assess the reliability of model fit and parameters
- Introduction to bootstrap and parametric bootstrap techniques, estimation of uncertainties, assessing relative and absolute fit to the data.
Imputing missing values and estimating ancestral states
- Introduction on how to estimate missing values and ancestral states; formatting the data, etc.
Inferring dependencies and causal links between traits evolving on trees
- Introduction to inferring graphs of dependencies between evolving traits (e.g. partial correlations, graphical LASSO, etc.)
- Using multivariate models to infer “causal” links (e.g, comparative study on sexual dimorphism)
Working on non-ultrametric trees (e.g., fossil data, virus strains)
- Introduction on identifiability issues and the strength and weaknesses of working with non-ultrametric trees
- Illustration with worked examples [and students’ datasets]
Transformations and data pre-treatments
- Discussions on the use of pre-transformations (eg log-transformation) and data reduction techniques (PCA, phylogenetic PCA) in comparative datasets; measurement error and intraspecific variance.
(Digression) Modelling multivariate time series
- Illustration on how to model multivariate traits on time-series rather than phylogenetic trees in mvMORPH (inferring trends, causal links, etc.)
Summary
- Summary on the various techniques’ strengths and weaknesses, model assumptions, and alternative tools currently available on R with some worked examples.

Assumed Background

Graduate or postgraduate degree in Biomedical, Life or Earth Sciences.

This course is not introductory: knowledge of multivariate statistics and R at user level is required to follow the course. Familiarity with univariate phylogenetic comparative methods is highly recommended.

Although not a requirement, participants are encouraged to bring along their own dataset with a matching phylogenetic tree (or sample of trees).

Software

Participants should have R and RStudio (recommended) installed on their computers.

Dates & Schedule

Online live sessions from 10:00 to 12:30 and from 13:00 to 15:00 (Madrid time zone).

Total course hours: 30 (22.5 hours of online live lessons, plus 7.5 hours of participants working on their own).

This course is equivalent to 1 ECTS (European Credit Transfer System). The recognition of ECTS by other institutions depends on each university or school.

Format

In the live sessions we will combine online lectures with hands-on computational exercises, in R.

Live sessions will be recorded. However, attendance to the live sessions is required to obtain the course certificate.

This course will be delivered in English.

Instructor

Dr. Julien Clavel
CNRS
France

Registration Fees

Course Fee
Early bird:
486 €
(388.80 € for Ambassador Institutions)
Regular:
650 €
(520 € for Ambassador Institutions)
Prices include VAT.
After registration you will receive confirmation of your acceptance on the course.
Payment is not required during registration. Check discounts here.
How do I get the Ambassador Institution discount?
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