Course Single Cell and Spatial OMICS – Advanced Course (module 2)

The course B321/01 Single Cell and Spatial OMICS – Advanced Course (module 2) is being offered by the Graduate School of Health, Aarhus University, 2023.

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).

Recommended knowledge for participation (if any):
Genetics, molecular biology, cell biology, genomics, bioinformatics

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 and spatially resolved resolutions.
Technologies enabling high-throughput single-cell and spatial 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 and spatial 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/spatial RNAseq data.
These skills can be applied to the study of transcriptomics at single-cell and spatially resolved resolutions.

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 Advanced Course (Module 2) will focus on introducing the theory, methodology, and pipelines of single cell and spatial RNAseq data analysis technologies.

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 and spatial scRNAseq data.
2. Understand the principle of different computational tools in single cell/spatial 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 and spatial sequencing.

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

Workload:
The full workload of the course is expected to be 55 hours

Head of course:
Lin Lin

Instructors:
Lin Lin, lin.lin@biomed.au.dk
Per Qvist, per.q@biomed.au.dk
Dan Fabricius Søndergaard, das@genome.au.dk
Juan Luis Garcia Rodriguez, juanlgr@biomed.au.dk
Xiangning Ding, xiangningding@biomed.au.dk

Venue: 
27 November in building 1231-114
28 November in building 1231-232
29 November in  building 1231-216
30 November in building 1231-232
01 December in building 1613-109

Participation in the course is without cost for:

• PhD students, Research Year students and Research Honours Programme 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:

• 27 November 2023 09:00 - 16:00
• 28 November 2023 09:00 - 16:00
• 29 November 2023 09:00 - 16:00
• 30 November 2023 09:00 - 16:00
• 01 December 2023 09:00 - 16:00