Course Single Cell RNAseq data analysis

The course B321/03 Single Cell RNAseq data analysis is being offered by the Graduate School of Health, Aarhus University, 2025.

Criteria for participation: 
University degree in medicine, dentistry, nursing, or Master’s degree in other fields and/or postgraduate research fellows (PhD students and research-year medical students).

Aim: 
The analysis of DNA mutations and gene expression (from DNA to protein level) in tissues and organs has transitioned from bulk to single-cell resolutions. Technologies enabling high-throughput single-cell genomic, transcriptomic, and proteomic (OMICS) analyses have developed rapidly in recent years. Crucially, these technologies have become essential tools for contemporary research in life sciences, biology, and medicine.

Given this, there is a need to systematically introduce PhD students to the processes of analysing, interpreting, and critically evaluating single-cell OMICS data. This becomes particularly relevant once students have completed the basic course and wish to gain more experience in bioinformatics analysis, including understanding and practicing the pipelines for transcriptomics data analysis.

The aim of this PhD course is to familiarise students with the principles, pipelines, and coding involved in the analysis of single-cell RNA sequencing data. These skills can be applied to the study of transcriptomics at single-cell resolution.

This course will introduce various data analysis pipelines, including data clean-up, normalization, differential gene expression analysis, GO enrichment analysis, and cell-cell communication analysis. The course is organized in collaboration with the national roadmap centre, CellX.

The Single Cell RNAseq data analysis will focus on introducing the theory, methodology, and pipelines of single cell RNAseq data analysis.

Learning outcomes:
By the end of the course, students are expected to:

1. Articulate and demonstrate systematic knowledge of the data analysis for single cell RNAseq data.
2. Understand the principle of different computational tools in single cell data analysis.
3. Comprehend and work on the individual steps of the analysis pipeline for single cell RNA sequencing data.
4. Re-analyse the data from studies within the field of single cell RNAseq. 

Workload:
The full workload of the course is expected to be 35 hours

Content: 
The course covers:

• Introduction to GenomeDK
• Sequencing data alignment
• Introduction to R/Bioconductor
• Installation packages for data analysis
• Data structures in Seurat
• Data clean up
• Differential gene expression analysis
• GO enrichment analysis
• Cell-cell communication analysis
• Trajectory analysis
• Visualization of data

Instructors: 

Lin Lin

Dan Ariel Søndergaard 

Lei Cheng

Diana Sharysh

Xiangning Ding

Mohamed Hassan

Venue: Aarhus University, Aarhus (other)

Participation in the course is without cost for:

• PhD students, Health Research Year students from Aarhus University
• PhD students enrolled at partner universities of the Nordoc collaboration
• PhD students from other institutions in the open market agreement for PhD courses

Course dates:

• 01 December 2025 09:00 - 16:00
• 02 December 2025 09:00 - 16:00
• 03 December 2025 09:00 - 16:00
• 04 December 2025 09:00 - 16:00
• 05 December 2025 09:00 - 16:00