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Biostatistics in R II



Course format Blended
Datum 2020-10-19 - 2020-12-16

Course introduction

Modern experimental platforms nowadays deliver the quantification of pools of biological molecules. Their analysis requires complex bioinformatics pipelines to obtain biologically relevant results. The students will use the acquired knowledge to design and apply work flows that handle omics data sets. The course consists of a theoretical and an extensive practical part, with the objective to provide advanced understanding of data analysis with R scripts and application of bioinformatics tools.

The course will introduce the students to advanced programming of R scripts necessary to deal with data from modern high-throughput experiments and gives a broad overview of tools for biological interpretation. Exercises involve in-depth application of standard pipelines to process omics data and a final project to apply the acquired abilities on real data that might come from experiments previously carried out by the student, e.g. during their bachelor/master thesis.


The following main topics are contained in the course:

  • statistics for large data sets
  • different types of data modeling
  • advanced data visualization
  • advanced data interpretation
  • computational tools for protein characterization
  • standard work flows for data from omics experiments


Students taking the course are expected to:

  • Have knowledge in statistics 
  • Understand the basic principles of molecular biology
  • Have basic programming skills in R
  • Know the fundamentals of biostatistics

Learning outcomes

The learning objectives of the course are that the student demonstrates the ability to:

  • independently analyze even conceptually demanding data sets. 
  • work with large data amounts and carry out standard statistical analysis to identify relevant features. 
  • use standard algorithms for multi-variate analysis
  • design scripts for detailed visualization of their results. 
  • know and apply tools for data interpretation.
  • know and apply standard pipelines for the processing of omics data.
  • know how to objectively discuss applied data analysis methods presented e.g. in publications.


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