Examination, exercises are evaluated in order to obtain admission for the examination
(50% of all achievable points)

This module teaches the technical fundamentals of technologies for the analysis of expression data, both at the RNA and protein levels. The focus will be on the RNA level, but a substantive goal will be to identify which common analysis techniques are applicable to so-called abundance data, such as those generated by typical transcriptomics and proteomics experiments. Topics include algorithms for designing experiments, normalization procedures (from raw to primary to normalized data, dimensionality reduction using principal component analysis as well as multidimensional scaling (MDS), clustering procedures, visualization of abundance data, simple statistical procedures of hypothesis testing (parametric and nonparametric tests, multiple testing, ANOVA) for differential gene expression, and classification methods (LDA).

Methods and acquired skills of the various modules of the first two years of study (e.g. algorithms, statistical methods, programming skills) are applied to concrete questions of an important topic area of bioinformatics. The students analyse expression experiments and learn how to program the scripting language R. They understand the connections between the different aspects of what they have learned so far and can apply it to practical problems. They are able to actively grasp problems, critically discuss them and create solutions. This increases the student's methodological competence.

Ausführliches Skript und ausgewählte Lehrbücher und Artikel