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Single cell RNA-seq data analysis
Date: 28.09.2017 9:00 - 29.09.2017 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.
Language: English
Lecturers: Tallulah Andrews (Sanger Institute)
Jenni Westoby (Sanger Institute)
Gioele La Manno (Karolinska Institutet)
Maria Lehtivaara (CSC)
Eija Korpelainen (CSC)
Price: The registration fee is 60 euros + VAT per day, and it includes morning and afternoon coffees. DPBM graduate school students register for free using their DPBM course form.
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 that no refunds will be made. Registration can be transferred to someone else from the same organization without additional charge.

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, so please have this available when registering.
Additional information

NOTE: The course and the waiting list are now full so we have closed the registration.

This hands-on course introduces the participants to single-cell RNA-seq data analysis concepts and tools. It consists of two alternative days which cater for different levels of computational skills. Depending on your background, please choose one of the following course days described below (you can't select both).

Please note that you can also participate in the Single Cell Symposium on 27.9 which takes place in Biomedicum 1 (Meilahti campus, Helsinki). Both the course and the symposium are organized in collaboration with the Doctoral Programme in Biomedicine (DPBM) of University of Helsinki. 

28.9. Single cell RNA-seq data analysis with R

Content: This course focuses on the analysis steps which are performed once we have a digital expression matrix. It covers cell quality control, normalization and batch effect correction, as well as downstream analysis such as feature selection, clustering and pseudotime analysis. The Scater package for R is used in the hands-on exercises. Teaching is provided by Tallulah Andrews and Jenni Westoby from the Sanger Institute.

Prerequisites: This course is intended for those who have basic familiarity with Unix and the R scripting language.We will assume that you are familiar with mapping and analysing bulk RNA-seq data.

29.9. Single cell RNA-seq data analysis with Chipster

Content: You will learn to preprocess DropSeq data (from raw reads in FASTQ files into a digital gene expression matrix) and how to do clustering analysis and find cluster marker genes with Seurat tools (10X Genomics data used in the exercises). Teaching is provided by Eija Korpelainen and Maria Lehtivaara, CSC. There is also going to be an introductory lecture about machine learning by Gioele La Manno, Karolinska Institutet.

Prerequisites: As the user-friendly Chipster software is used in the exercises, no previous knowledge of Unix or R is required, and this course is thus suitable for everybody.

Projects and Collaboration Networks
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Projects and Collaboration Networks
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