Course Objective

Biologists often have to handle, analyze, and present analysis results
of large sets of biological data, originating from genomics,
transcriptomics, proteomics, and metabolomics experiments. In many
cases, these tasks cannot be performed using standard “press the button”
commercial statistical packages. A popular solution to this problem is
the use of the open source statistical programming environment R. R is
used intensively in the community of experimental biologists, and most
newly published data analysis techniques are first available as
R-packages.

Goals:
- To obtain knowledge of the structure of the R-language as well as
practical skills in the programming, producing graphs and documents with
R
- To obtain hands on experience in handling and investigation of data
sets
- To obtain an overview of some modern statistical learning techniques

Course Content

This course focuses on obtaining the practical skills to perform data
handling and analysis tasks from small to large data sets, and to
graphically display and interpret the results. Statistical analyses will
center on multivariate analysis. Examples of the generation and
interpretation of (large) data sets will be presented from the various
fields of biology, ranging from cell biology to ecology. The items
treated during the lectures will be studied in computer practical
sessions using R.

Teaching Methods

Lectures, tutorials, computer practical work, self-study

Method of Assessment

Assessment will take place by three individual assignments.

Literature

A course syllabus with theory and computer exercises will be made
available

Target Audience

Master students having a background in basic statistics

Recommended background knowledge

Knowledge of basic statistics and some experience with a programming
language