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Objectives:

Provide students with statistical knowledge and tools to manipulate, analyze and visualize biological data with R. Introduction to modelling, simulations and Bayesian statistics. 

Topics:

  • Refresher into R 
  • Exploratory analysis for ecology and evolution (Principal Component Analysis) 
  • Linear regression and ANOVA 
  • Hypothesis testing using bootstrap and permutations 
  • Introduction to analysis of population genetics in R 
  • Modelling and simulation of dynamics systems 
  • Bayesian statistics and advanced inference algorithms (Markov chain Monte Carlo)
  • Students case studies

Participants have to be present at 85% of the contact hours (this means that they can miss one half-day), and actively participate in all activities.

This course can give credits to PhD programmes at CIÊNCIAS or of programmes with partnership from CIÊNCIAS and other institutions with 6h-7h of contact hours per ECT, as a function of specific requirements. For these students additionally to the exercises done during the week the delivery of a written report done after the course is mandatory. For programmes with less hours of contact per ECT (6h/ECT, getting 6 ECTs from the course) students need to do an additional assignment (summary report). If needed 1 or 2 additional hours of contact may be added. Such report(s) are also advised for other students requesting accreditation of the course in their institutions.

Minimum formation: Knowledge of R programming syntax and Rstudio. Preference will be given to participants that attend the introductory course in R or that have previous knowledge in R. 

Directed to: PhD or MSc students in Biology, Evolution, Ecology or related areas, and postdocs and other professionals working in related topics 

General Plan

Refresher into R. A very quick refresher on R language to be sure that all students know basic R programming 

Exploratory analysis for ecology and evolution (Principal Component Analysis). The students will learn how to perform and interpret the results of Principal Component Analyses (PCA) and Multivariate Evolutionary Analyses. 

Linear regression and ANOVA (factorial and nested ANOVA). Students will learn how to perform and interpret results from general linear models.  

Hypotheses testing using resampling approaches (e.g. bootstrap and jackknife). Many datasets in ecology and evolution violate assumptions of classical statistical tests, and hence resampling approaches became common practice. Students will learn how to perform hypothesis testing using permutation tests, bootstrap and jackknife.  

Introduction to analysis of population genetics in R: the students will learn how to compute the basic genetic statistics (genetic diversity, FST), test Hardy Weinberg equilibrium, compare genetic diversity between populations and perform PCA with genomic data. 

Modeling and simulation of dynamics systems. We will use examples from population genetics (genetic drift) and population dynamics (Lotka-Volterra predator-prey model) to illustrate how R can be used to perform simulations. The students will learn how to model and perform simulations of populations evolving through time.  

Introduction to Bayesian statistics. The students will learn how to use a Markov chain Monte Carlo (MCMC) algorithm to estimate parameters of different models of population dynamics. 

Student’s case studies. 

Throughout the course, students will be encouraged to apply the knowledge obtained to data from their own work. Depending on the interests of the participants some of the content can be adjusted to focus more on examples from evolutionary biology or from ecology. On the last day, each student will do a small presentation with data available from their own work, or with possible applications to their research question. 

Fees

Free for 1st year PhD students in Doctoral programmes at CIÊNCIAS (e.g. Biologia), Biodiversity, Genetics and Evolution (BIODIV UL; UP) and Biology and Ecology of Global Changes (BEAG UL, UA), when the course counts credits for their formation, in which case the delivery of a final report done after the course is mandatory; the course is also free for more advanced PhD students of the BIODIV programme (ULisboa or UPorto); 60 € for more advanced PhD students of CE3C; 100 € for PhD students of the PEERS network not from CE3C (CFE); 150 € for CIÊNCIAS Master students and unemployed; 200 € for BTI, BI and other PhD students with scholarship*; 300 € for Professional and postdocs.  

(* For PhD students also doing the Introduction to R course the fee is reduced to 150 €) 

When the maximum number of students is reached, 10 vacancies will be available for non-paying 1st year PhD students mentioned above, being, by order of preference students from: 1) CE3C; 2) BIODIV (not from CE3C); 3) CIÊNCIAS (not from CE3C); 4) BEAG (not from CE3C or CIÊNCIAS). 

How to Apply

Candidates should fill in the APPLICATION FORM, which will be available in this section when the call opens. 

This form is strictly confidential and will only be used for the purposes of this application.   

When filling the form mind to:  

  • FILL ALL THE MANDATORY FIELDS
  • UPLOAD CV AND MOTIVATION LETTER, both mandatory.

For any doubts please contact the coordinator of the CE3C courses (and teacher in this course), Inês Fragata (irfragata@ciencias.ulisboa.pt).