PhD course: Single-cell and spatial omics

A 4 day course on Single-cell and spatial omics

Language

English 

Form

Dry-lab course, lectures, group work, exercises and discussions

Please be aware that the course starts on SUNDAY 11 May. 

Learning objectives

1. To understand single-cell transcriptomics, epigenomics, and proteomics technologies
2. To understand spatial transcriptomics and proteomics technologies
3. To understand CRISPR-based perturbation technology at single-cell level
4. Dry lab: To understand how to preprocess single-cell transcriptomics data, annotate, perform differential analyses, gene regulatory networks, and cell-cell interaction
5. Dry lab: To understand how to reprocess spatial transcriptomics data, segment cells, annotate, and integrate with single-cell transcriptomics

Content

• Introduction to principles, limitations, and challenges in single-cell transcriptomics, epigenomics, and proteomics across different methods
• Introduction to principles, limitations, and challenges in spatial transcriptomics and proteomics across different methods
• Introduction to the bioinformatic analysis of single-cell and spatial transcriptomics data, in particular processing of single cell data using for example Seurat, Conos, Pagoda, and Baysor (for the data processing part, the participants will be subdivided into beginner and advanced users)

Course directors

Konstantin Khodosevich, Professor, BRIC, UCPH konstantin.khodosevich@bric.ku.dk 

Tune Pers, Associate Professor, CBMR, UCPH tune.pers@sund.ku.dk 

Invited external speakers (preliminary list)

Ermelinda Porpiglia, AU

Paolo Cadinu, Harvard Medical School

Jonathan Brewer, SDU

Marek Bartosovic, Stockholm University

Xin Jin, Scripps Research

Registration

Please register before 1.4.2025. Please send letter of motivation for attending this course before registration deadline to: momed@bric.ku.dk . Mention following:

whether you are already using single cell technologies, how important these are for your PhD project and what are your skills in computational analysis of SC data.