This hands-on course in Zoom introduces single-cell RNA-seq (scRNA-seq) data analysis methods. It covers the processing of transcript counts from quality control and filtering to dimensional reduction, clustering, cell type identification and cluster marker gene detection. You will also learn how to do integrated analysis of multiple samples.
Both course days are 9:00-12:30 Finnish time (8:00-11:30 CET).
The course consists of lectures and exercises. The lectures will be pre-recorded, and participants are requested to view the videos prior to the course and test their knowledge with a set of questions. This gives you more time to reflect on the concepts so that you can use the classroom time more efficiently for discussions and exercises.
In the exercises we use Seurat tools embedded in the free and user-friendly Chipster software, so no experience in R is required, and the course is thus suitable for everybody who is planning to use single-cell RNA-seq.
You will learn how to
- perform quality control and filter out low quality cells
- normalize gene expression values
- remove unwanted sources of variation
- select highly variable genes and perform dimensionality reduction (PCA)
- cluster cells
- visualize clusters using UMAP and tSNE
- identify cell types using reference-based SingleR
- find marker genes for a cluster
- integrate multiple samples
- find conserved cluster marker genes for two samples
- find genes which are differentially expressed between two samples in a cell type specific manner
- visualize genes with cell type specific responses in two samples
- Links to slides, lecture videos and exercises
Maria Lehtivaara, Eija Korpelainen and Iida Hakulinen (CSC)
Should you have any questions, please don't hesitate to contact email@example.com.
29.5.2023 09:00 +03:00 EEST
30.5.2023 12:30 +03:00 EEST