Applied Single-Cell Bioinformatics, PhD

• independently seek out and obtain relevant information about single-cell data analysis.
• list various single-cell technologies along with relevant biological use-cases.
• discuss strengths and weaknesses for various single-cell technologies.
• assess strengths and weaknesses with various experimental setups.
• list various bioinformatic analysis tools and what they are used for.
• discuss strengths, weaknesses, and key parameters for various analysis approaches.
• apply and interpret state-of-the-art bioinformatic analysis to single-cell data in the biological context of the data.
• use R to organize, modify, and visualize data.
• as a group design and execute a single-cell analysis project.
• clearly present obtained results orally using proper visual aids.

The first part of the course will introduce and have you work with various single-cell technologies and analyses, including, but not limited to:
• Single-cell RNA-seq
• Dimensionality reduction, integration, clustering, and cell-type annotation
• Downstream analysis such as differential expression and differential cell type abundance
• Multimodal single-cell analysis
• Spatial transcriptomics

In the second half of the course, you will have to do a group-based project on real data. For this project, you can work on data we provide or bring your own data (e.g., from your master thesis or PhD project) as long as the dataset has been approved by a teacher.

22100/22101/22126, Knowledge of cells, cell types, cell structure, their biological function, subcomponents, biochemical and molecular processes (metabolism, RNA- and protein synthesis). Knowledge of next generation sequencing (NGS), mapping, and mapping QC.

For the entire course, we will use R to organize, analyze, and visualize data. This also means we expect that you are already comfortable with programming in R. You do not have time to learn that during this course. If you do not know how to solve basic data manipulation tasks in R (examples listed below), you will need to learn this BEFORE taking this course in order to be able to keep up.

Examples of data analysis you need to know how to do in R prior to course start (without looking it up online):
• Merge two datasets based on a shared identifier
• Do a per-group operation of data based on a grouping column
• Do a per-row or per-column operation without using loops
• Use ggplot2 to visualize data, e.g., a scatterplot where points are colored by category

The course is run as a “flipped classroom”, i.e., you prepare from home by reading material, watching lectures, etc., to ensure a larger proportion of the in-class time can be used on applied computational exercises.

Minimum: 10.

Please be aware that this course will only be held if the required minimum number of participants is met. You will be informed 8 days before the start of the course, whether the course will be held.