Advanced Biological Data Analysis

This course offers an indepth overview of statistical methods for advanced biological data analysis. The course consists of a series of lectures, case studies and computer laboratories that cover good practice in statistics and biological data analysis. Topics include general and generalized linear models, categorical data analysis, parametric and non-parametric statistics, and multivariate statistics.

The course consists of two major parts:

Part I - A revision of basic methods for biological data analysis, including (1) Introduction to R; (2) Pearson correlation;

(3) T-test; (4) Simple linear regression; (5) Model diagnosis and influential observations; (6) One-way between group ANOVA; (7) Multiple linear regression and interaction; (8) Multiway between group ANOVA; (9) ANCOVA; (10) Nonparametric statistics; (11) Analysis of contingency tables; and (12) Data visualisation.

Part II - Advanced biological data analysis: 1 - Complex ANOVA designs: repeated measures ANOVA, complex ANOVA designs, nested ANOVA and linear mixed models, model selection, 2 - Generalized linear models: logistic regression, generalized linear models, generalized linear mixed models, 3 - Advanced regression techniques: polynomial regression, nonlinear regression, and nonlinear mixed models, 4 - Multivariate statistics: PCA and biplot, non-metric multidimensional scaling and cluster analysis, 5 - Special topics

Practical Information: Part I includes a series of videos which you have to watch before each session. The sessions focus on data analysis in practise using the statistical software R.

Part II consists of classical classroom sessions (session 1-4), and “special topic” sessions in the form student presentations on an advanced statistical method of choice. For this you choose a topic from the list of "special topics" (see below), and you prepare and deliver a 45 min lecture on this topic, combining theory + exercises. You will receive close guidance for the preparation of your presentation.

The special topics to choose from include (1) Spline based regression technique; (2) Generalized additive models; (3) Survival analysis; (4) Nonparametric statistics, bootstrapping and permutation tests; (5) Bayesian inference; (6) Discriminant analysis; (7) MANOVA; (8) Canonical ordination; (9) Experimental design; (10) Power analysis; (11) Another quantitative method that is relevant for your study in the biosciences (to be confirmed with the course coordinator).

Evaluation

In order to pass the course, you have to fulfil the following aspects of the "portfolio":

1. 80% attendance of lectures and practice sessions. It is possible to attend the course remotely.
2. An oral presentation (= your special topic presentation)
3. A written report. You analyse a biological dataset (preferably your own data or data from a project you are involved in), interpret the results, and summarize your analyses in a report. You deliver the report within a month after the end of the course.

Within the written report you should demonstrate that you are able to:

1. Adequately/independently perform data analysis, especially that you can adequately argue the statistical test of choice to address the biological question(s), correctly test assumptions of the statistical method(s) used, correctly implement the statistical method(s) using statistical software, correctly interpret statistical results and figures, and draw correct biological conclusions from your results. Lack of fulfilment of any of these criteria could be a reason to fail the assignment.
2. Independently produce a high quality report, where you adequately and understandably explain your hypothesis, what data you collected, how you analysed the data, which results you obtained, and what biological conclusions you infer from the results. A critical reflection on your methods and results is also required.