91400 - BIOMETRIA EVOLUZIONISTICA ED ECOLOGICA

Learning outcomes

At the end of the course, the student can handle biologic data-bases and analyse data using the most important biometric methods. The course comprises a collection of basic statistic techniques (t-test, chi-squared test, linear regression, ANOVA, etc.) and widely used multivariate methods (PCA, MDS, cluster analysis, etc.), including simple applications to genetic data. Moreover, the student learns the principal functionalities of the program R, one of the reference softwares for statistical computation in the scientific community, which will be applied to all the arguments performed during the course.

Course contents

1 - The R software. Basics, data handling, plots.
2 - Probability and distributions. Discrete distributions (binomial). Continuous distributions (normal). Densities, cumulative probability, quantiles, confidence intervals.
3 - Statistic tests. Tests for comparing continuous variables (t-test, Wilcoxon test). Tests for discrete variables/tabular data (chi-squared, Fisher test).
4 - Simple linear regression and correlation. Analysis of Variance (ANOVA) and Kruskal-Wallis test.
5 - Multivariate analyses 1. Principal Component Analysis. Distance matrices. Mantel test. Multi-dimensional Scaling. Applications to molecular data.
6 - Cluster analysis. Hierarchical methods and non-hierarchical methods. Linear discriminant analysis. Discriminant analysis of principal components (DAPC).
7 - Simple applications to population genetics (libraries ape, ade 4, adegenet, pegas, poppr). Hardy-Weinberg equilibrium. Mismatch Distribution. Tajima test. R2 test. Inbreeding. Fixation indices. Analysis of Molecular Variance (AMOVA).

Readings/Bibliography

P. Daalgard. Introductory statistics with R. Second Edition. Springer, 2008.

Teaching methods

The course is characterised by absence of separation between frontal lessons and laboratory activity. On the contrary, the considered biometric/biostatistical methods are teached with a focus on practical aspects, by means of in-lab examples and exercises.

Assessment methods

The final exam is aimed to the evaluation of the achievement of the following didactic goals:
- exhaustive knowledge of the statistical/biometric tools introduced during the lessons;
- ability to use the mentioned tools to analyse biologic datasets;
- ability to interpret the obtained results (in light of the studied biologic phenomenon).

The final exam includes:
- solution of a biologic/biometric problem using the R software (the exercise will be sent by mail to all the participants one week before the exam);
- oral discussion of the obtained results;
- oral questions about the program of the course.

Teaching tools

Laboratory of informatics. Pdf slides. Example data.

Office hours

See the website of Alessio Boattini