This is the 2019 website for the course Statistical methods in bioinformatics held by the University of Copenhagen.

## Practical information

• The course will start on Monday, April 8th and end on Friday, April 12th.
• Each day will take place at CSS (Kommunehospitalet), and we will be in room 15.3.15 (building 15, 3rd floor, room 15) (see map at the end of this document). Note: This room is slightly tricky to find. Go to the 2nd floor of the intersection of buildings 5, 10, and 15. There you will see signs to the room 15.3.15.
• The course will generally run every day from 8.15 to 15 with a lunch break in between (see the programme for more information).
• 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 slightly 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.

### Installation

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.

• Cytoscape

Installation instructions are available on the pages above.