This course will run from 16th – 20th August, Millport Field Station,
Ilse of Cumbrae, Scotland
This course will provide an extensive overview of exploratory methods
for the analysis of genetic data using the R software and aim to equip
participants with powerful resources for tackling increasingly common
challenges in genetic data analysis.
The course is aimed at PhD students, research postgraduates, and
practicing academics as well as persons in industry working with genetic
data in fields such as molecular ecology, evolutionary biology, and
phylogenetics. This course will provide a comprehensive introduction to
various statistical approaches for the analysis of genetic data.
Course content is as follows
Day 1 Introduction to phylogenetic reconstruction
• Lecture 1a: Reconstructing phylogenies from genetic sequence data.
Three main approaches covered: distance-based phylogenies; maximum
parsimony; and likelihood-based approaches.
• Lecture 1b: Short R refresher.
• Practical 1: Phylogenetic reconstruction using R. Three main
approaches plus rooting a tree; assessing/testing for a molecular clock;
Main packages: ape, phangorn.
Day 2 Introduction to multivariate analysis of genetic data
• Lecture 2: Key concepts in multivariate analysis. Focus on using
factorial methods for genetic data analysis.
• Practical 2: Basics of multivariate analysis of genetic data in R.
Topics include: data handling, population genetic tests of population
structure (PCA, PCoA).
Main packages: adegenet, ade4, ape.
Day 3 Exploring group diversity
• Lecture 3: Approaches to identifying and describing genetic
clusters. Topics include: hierarchical clustering, K-means,
population-level multivariate analysis (between-group-PCA, DA, DAPC).
• Practical 3: Applying the approaches covered in morning lecture and
emphasising their strengths and weaknesses.
Main packages: adegenet, ade4.
Day 4 Spatial genetic structure
• Lecture 4: Discussing the origin and significance of spatial genetic
patterns, and how to test or them.
• Practical 4: Visualising and analysing spatial genetic data. Topics:
spatial density estimates, Moran/Mantel tests, mapping principal
components in PCA, spatial PCA.
• Main packages: adegenet, glmnet.
Main packages: adegenet, glmnet.
Day 5 Using R for reproducible science
• Lecture 5: Using R for reproducible science.
• Practical 5: Practical session based on morning lecture
• Main packages: knitr, Sweave, rmarkdown
• Option to discuss own data (time permitting)
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