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Genetic data analysis using R (GDAR01)
3rd August 2015 - 7th August 2015
This course will provide an extensive overview and comprehensive introduction to exploratory methods and various statistical approaches for the analysis of genetic data using the software R 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.
A mixture of lectures and hands-on practicals. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
A basic understanding of concepts in population genetics and the statistical analysis of genetic data.
Assumed computer background
Previous experience with data analysis using R is required such as the ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.
Equipment and software requirements
A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links.
It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available.
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Meet at the Tullie Inn, Balloch at approximately 18:30 before being taken by minibus to SCENE ( Download directions PDF )
Monday 3rd – Classes from 09:00 to 17:00
Intro to phylogenetic reconstruction.
Module 1a: Reconstructing phylogenies from genetic sequence data. Three main approaches covered: distance-based phylogenies; maximum parsimony; and likelihood-based approaches. Module 1b: Short R refresher. Practical 1: Phylogenetic reconstruction using R. Three main approaches plus rooting a tree; assessing/testing for a molecular clock; and bootstrapping. Main packages: ape, phangorn.
Tuesday 4th – Classes from 09:00 to 17:00
Intro to multivariate analysis of genetic data.
Module 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.
Wednesday 5th – Classes from 09:00 to 17:00
Exploring group diversity.
Module 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.
Thursday 6th – Classes from 09:00 to 17:00
Genome-Wide Association Studies (GWAS).
Module 4: Intro to GWAS study design and statistical approaches: univariate, regression-based and multivariate analysis. Practical 4: Applying each class of methods covered in morning lecture, with emphasis on their strengths and weaknesses. Main packages: adegenet, glmnet.
Friday 7th – Classes from 09:00 to 16:00
Spatial genetic structures.
Module 5: Discussing the origin and significance of spatial genetic patterns, and how to test for them.
Practical 5: Visualising and analysing spatial genetic data. Topics: spatial density estimates, Moran/Mantel tests, mapping principal components in PCA, spatial PCA. Main packages: adegenet, adehabitat, ade4.