Responsible teachers:

• Carl Herrmann (carl.herrmann@bioquant.uni-heidelberg.de) - IPMB & BioQuant
• Maiwen Caudron-Herger - [DKFZ]
• Daria Doncevic (daria.doncevic@bioquant.uni-heidelberg.de) - IPMB & BioQuant

The goal of the Data Analysis Module during the Summer Semester is to provide hands-on experience in data analysis of large scale datasets and get first insights into using computational tools to provide a reproducible data analysis.

After this module, you will have

• gained skills in programming language such as R or Python (depending on the chosen project)
• learn to use tools to perform reproducible analysis, like Markdown/Notebooks and GitHub
• understand the key steps in a large-scale data analysis

• choose your team and one project until Monday 20.04 (coordination: semester speakers)
• prepare and present a 10 min. project proposal on Wednesday 13.05
• work on your project during the whole term
• meet your tutor every week (max. 30 min per group)
• get your repository completed until Monday 13.07
• give a final poster presentation on Wednesday 15.07

• Introductory lecture Wednesday 15.04

  • overall presentation of the module
  • presentation of the different topics and sub-projects
  • introduction to Markdown / Github

• Tutorial Wednesday 22.04

  • More specific information about the presentations and evaluations
  • Introduction to visual communication for scientists (slides, poster)

We have defined 5 topics in data and image analysis.
Each project will comprise up to 5 different sub-projects.
Most of the time, these 5 sub-projects are very similar to each other but analyze slightly different datasets.

You can find a description of the 5 topics here:

• Topic 01 : Biomedical Image Analysis (Karl Rohr & Leonid Kostrykin - Python)

  • Tutor: Levente Temesvay-Nagy levente.temesvari-nagy@stud.uni-heidelberg.de

• Topic 02: Cancer Hallmarks (Carl Herrmann - R)

  • Tutor: Duc Thien Bui duc-thien.bui@bioquant.uni-heidelberg.de

• Topic 03: DNA Methylation (Michael Scherer - R)

  • Tutor: Paul Christmann paul.christmann@stud.uni-heidelberg.de

• Topic 04: Gene Regulation of Immune Cells (Alexander Sasse - Python)

  • Tutor: Simon Westermann simon.westermann@stud.uni-heidelberg.de

• Topic 05: Somatic mutations in cancer (Daniel Hübschmann - R)

  • Tutor: Niklas Schmidt niklas.schmidt@stud.uni-heidelberg.de

• Topic 1 is an image analysis project which will be performed in Python.
• Topics 2,3,4,5 are data analysis topics/projects which will be conducted in R or Python as indicated above.

How do I select my project/team?

• check the project description on this page (see above)!
• select your team mates; each sub-project will be worked out by groups of 4 students.
• once the choice has been made, register your team in a Google Spreadsheet (see below); the choice of sub-project and definition of the teams should be completed by Monday, 20.04.26, 10 am (no extension!)
• create a GitHub account and register your github name in the registration Google Sheet (coordinated by the semester speakers)

For each project, there will be a tutor assigned to this project. Each team within a project will have a weekly meeting with its tutor on Wednesday between 10 am and 1 pm during 20-30 minutes.

VERY IMPORTANT: as the weekly time which the tutor can dedicate to your project is limited, you should carefully prepare your meeting.

• select your project and you team mates and register before Monday, 20.04.2026, 10 am
• Project presentation on 13.05.26: prepare a 10 minutes presentation (+10 minutes discussion/questions) based on the indicated literature for each project, listing the relevant questions/topics that you want to address in your project. During this presentation, you should also explain the datasets you will be working with, and how you want to make use of them.
• Final RMarkdown/Jupyther notebook (should be placed in the GitHub repositrory) until 13.07.26 12pm latest as the repositories will be closed.
• Final presentation on 15.07.26 (10 min poster presentation + 10 minutes discussion/questions)

Each student will have an individual evaluation! This will take into account the 2 presentations listed above.

Here are the relevant points taken into account during the project proposal presentation:

• presentation of the literature results
• understanding of the biological question
• clarity of presentation of planned analysis and milestones
• knowledge of the datasets
• teamwork aspects
• quality of the oral presentation, slides and poster
• During the oral presentations, each student will be asked to explain part of the analysis, especially to explain the code! So everyone should make sure to be involved in the project.

Final script will be submitted (or “committed”) to the Github repository of the group as a .Rmd or .ipynb file.

Important note: Science is collaboration! so please make sure to share your insights/knowledge with other groups! You are free to choose whatever way to do so, e.g. Whatsapp groups or Slack groups.

Practical aspects
Depending on the projects, you will use either R (Topics 02/03/05) or python (Topics 01/04).

R-based projects
You will use RStudio, and create a R markdown document.

• RStudio
  For those of you who want to work on their laptops, you can install RStudio using the previous link; however, since some datasets are rather large, you will need a decent laptop to be able to process the data in a reasonable time (i5/i7 CPUs with 8Gb RAM at least)

• RMarkdown documents
  These will consist in a mixture of plain text (explanations about the analysis, comments,…) and code pieces (called chunks). All plots will be automatically and dynamically created from the code pieces in the markdown document.

Have a look at this tutorial or this one to get started with RMarkdown; RMarkdown is very easy to generate with RStudio.

Python based projects
Similar to R markdow documents, the Jupyter Notebook offers a way to mix markdown text together with Python code. Installing Jupyter Notebook requires the installation of Python. Just follow the instructions on the previous link.

GitHub
Git is a system to handle collaborative projects, in which each member of the team is contributing to the project. You can check this website for a simple intro to Git/GitHub.
Git can be used either from the command line, or using GitHub Desktop, a GUI manager which makes commiting changes, etc… very easy.

This tool will help you (and us…) track the progress of your project.

Introduction to GitHub

Intro to GitHub
Push / Pull changes in GitHub
Resolve conflicts