This is the 2021 website for the course Statistical methods in bioinformatics held by the University of Copenhagen.

Practical information

  • The course will start on Monday, April 12th and end on Friday, April 16th.
  • Due to the ongoing situation with the Covid-19 virus we will run the course completely online by streaming lectures and running exercises via zoom. See more information about Zoom below.
  • The course will generally run every day from 8.15 until around 15. The format of the individual days might vary slightly so be prepared to be flexible as possible.
  • Teachers for the course will be Claus Ekstrøm, Lars Juhl Jensen, and Stefan Seemann.

Additional information will be given on the first day.

We will not be following a specific textbook closely but recommend Advances in Statistical Bioinformatics: Models and Integrative Inference for High-Throughput Data and Regression with linear predictors. See the notes for more information.

Learning objectives

Bioinformatics is concerned with the study of inherent structure of biological information and statistical methods are the workhorses in many of these studies. Some of this inherent structure is very obvious and can be observed directly through correlations of patterns in high-dimensional data, while other patterns arise through more complicated underlying relationships.

This course covers some of the statistical models and methods suitable for analyzing high dimensional data - in particular high dimensional data that rely heavily on statistical methods The course will contain of equal parts theory and applications and consists of five full days of teaching and computer lab exercises. It is the intention that the participants will have a thorough understanding of the statistical methods and are able to apply them in practice after having followed this course.

A student who has met the objectives of the course will be able to:

  1. Analyze data from a bioinformatics experiment using the methods described below and draw valid conclusions based on the results obtained.
  2. Understand the advantages/disadvantages of the methods presents and be able to discuss potential pitfalls from using these methods.
  3. Discuss and develop new methods that can be used to analyze novel types of bioinformatics data.

Laptops and software

You should bring a laptop as we will not have access to the computer rooms at the university.


Before the course starts you should make sure that you have installed the latest version of:

  • R and have installed the packages: kinship2, coxme, glmnet, MASS, MESS, cluster, data.table, lme4, minerva, and PTAk. This can be done from inside R using the command:

    install.packages(c("kinship2", "glmnet", "coxme", "MESS", "MASS",
                       "cluster", "data.table", "lme4", "minerva", "PTAk"))

    R Studio is also highly recommended but is not necessary.

  • We also need some packages from BioConductor, namely edgeR, DESeq2,vsn, These can be installed by running the following R chunk:

    if (!requireNamespace("BiocManager", quietly = TRUE))
    requiredPackages = c('edgeR','DESeq2','vsn','')
    for (p in requiredPackages) {
        if(p %in% rownames(installed.packages()) == FALSE) {BiocManager::install(p)}
    requiredPackages = c('ggplot2','dplyr','NMF','grDevices')
    for (p in requiredPackages) {
       if(p %in% rownames(installed.packages()) == FALSE) {install.packages(p)}
  • Cytoscape

  • You could also try to install plink but that is not strictly necessary.

Installation instructions are available on the pages above.

Extra software might be installed through the course so make sure you have administrator/root access to your computer.

Using zoom

You will receive an email invitations to join zoom - we will use the same link for each day. The same link can also be found on the programme page. Please try to keep your camera on if internet bandwidth permits as that makes the experience nicer and more intimate than if we are just seeing blank screens.

Claus Ekstrøm 2021