BIF-51306 Biological Data Analysis and Visualization

Course

Credits 6.00

Language of instruction:

English

Assumed knowledge on:

BIF-50806 Practical Computing for Biologists

Continuation courses:

BIF-51806, Biological Discovery Through Computation

Contents:

Much biological data is quantitative, and there is a wide range of methods available for the analysis of such data. After a brief introduction to high-throughput measurement data and normalisation, a number of visualisation methods will be discussed. Next, methods will be introduced to find groups (clustering), dependencies (regression), significant differences between conditions (hypothesis testing) and to predict classes (classification). Finally, ways of assessing the biological relevance of findings and of interpreting results in terms of the underlying biology will be discussed. Students will learn to apply all these methods in practice in R.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- describe a number of normalization and visualization methods for specific types of data and purposes;
- qualitatively describe a number of analysis methods from statistics, clustering and classification;
- given a dataset and a biological question, select appropriate methods, apply these in R scripts and critically evaluate results;
- explain the influence of parameters on these results and discuss their significance.

Activities:

In 7 blocks, students will learn about normalization, visualization, clustering, regression, hypothesis testin, classification and interpretation. Each block consists of 4 days: 1) lectures and self-study, 2) lab work, 3) a project on real-world data and 4) self-study and a test. The course concludes with an integrative project and a final written examination.

Examination:

Final marks will be based on the marks obtained for the projects (25%) and tests (25%) in each block, as well as the marks obtained for the final project (25%) and the written examination (25%). For the final project and the written examination, the marks obtained should be at least a 5.0. Re-sitting separate tests or re-doing projects from each block is not possible. In case the final mark is not sufficient to pass, the final project and the written examination can be re-sit.

Literature:

Slides, handouts and book: Tony Fischetti, 'Data analysis with R', Packt, 2015. ISBN 9781785288142; as e-book via https://www.packtpub.com/