single-cell-chipster-2019 - Training
|Date:||24.09.2019 9:00 - 24.09.2019 17:00|
|Location details:||The event is organised at the CSC Training Facilities located in the premises of CSC at Keilaranta 14, Espoo, Finland. The best way to reach us is by public transportation; more detailed travel tips are available.|
Eija Korpelainen (CSC)
Maria Lehtivaara (CSC)
The fee covers all materials and morning and afternoon coffees.
Some of the seats are reserved for Doctoral Program in Biomedicine (DPBM) participants, whose participation fee is covered by the program.
Payment can be made with electronic invoicing, credit card, or direct bank transfer. Note that for electronic invoicing you need the operator and e-invoicing address (OVT code) of your organization. Please also note that invoice reference is needed for electronic invoicing in your organization.
If a cancellation is received five (5) business days prior to the course, the course fee will be refunded with the exception of a handling fee of 10 €. For no-shows and cancellations after the cut-off date no refunds will be made. Registration can be transferred to someone else from the same organization without additional charge.
This course introduces single cell RNA-seq data analysis methods, tools and file formats. It covers the processing of transcript counts from quality control and filtering to dimensional reduction, clustering, and differential expression analysis. You will also learn how to do integrated analysis of two samples. We are using the Seurat v3 tools embedded in user-friendly Chipster software.
Please note that you are most welcome to attend also the Single cell transcriptomics symposium in Biomedicum Helsinki 23.9.2019.
This event is part of the Single cell course week.
The free and user-friendly Chipster software is used in the exercises, so no previous knowledge of Unix or 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 variable genes
- perform dimensionality reduction (PCA, tSNE, UMAP, CCA)
- cluster cells
- find marker genes for a cluster
- integrate two 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
After this course you should be able to:
- use a range of bioinformatics tools to undertake basic analysis of single cell RNA-seq data
- discuss a variety of aspects of single cell RNA-seq data analysis
- understand the advantages and limitations of single cell RNA-seq data analysis