Section: 12. Test your skills | Single-cell RNA-seq data analysis with Chipster | csc

Main course page
  • About the course

    Course contents

    In this course, you will learn how to analyse single-cell RNA-seq data using the Seurat single-cell tools integrated in the easy-to-use Chipster software. The exercises and course data are based on the Seurat guided analyses "Guided tutorial - 2700 PBMCs" and "Introduction to scRNAseq integration".

    This course contains two types of lecture videos: short lectures on each topic by trainers from CSC (ELIXIR-FI), and more in-depth lectures by Paulo Czarnewski (NBIS / ELIXIR-SE), Ahmed Mahfouz (LUMC / ELIXIR-NL) and Jules Gilet (ELIXIR-FR).

    You will learn the following topics, and how to perform these steps in the Chipster software:

    • UMAP plot showing how cells (dots) are clusteredperform quality control and filter out low quality cells
    • normalize gene expression values (with global scaling normalization and SCTransform)
    • scale data and remove unwanted sources of variation
    • select highly variable genes
    • perform dimensionality reduction (PCA, tSNE, UMAP, CCA)
    • cluster cells
    • find marker genes for a cluster
    • annotate cells and clusters using a reference data
    • take a closer look at the Seurat objects
    • 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

    "It is so nice to be able to do the whole workflow in Chipster, compared to the old model, where I had to transfer the tsv file to R-studio and run Seurat there. -- I learned how to use the Seurat tools in Chipster and what all the steps really mean. I learned to check the results after every step to adjust the next steps parameters and to test different PCA plotting tools. I also learned how to find different genes in the clusters and how to visualize them. I never got this far using the R-pipeline. " -Pinja, course participant & PhD student from University of Helsinki

    Learning objectives

    After this course you should be able to:

    • use the Seurat tools available in Chipster to undertake basic analysis of single-cell RNA-seq data
    • name and discuss the different steps of single-cell RNA-seq data analysis
    • understand the advantages and limitations of single-cell RNA-seq data analysis in general and in Chipster

    Keywords: Chipster, Seurat, single-cell sequencing, RNA-seq, clustering, aligning cells, cluster markers

    Links to material
    The relevant material is linked in each course section. Here are some quick links:

    Each section of this course contains lecture videos, hands-on exercises and questions/tasks. The tasks can be used to confirm that you have reached the learning goals. You can use the Q&A Forum below to ask questions regarding the course topics or the exercises. Once you have finished all the tasks, you can download a course certificate with a unique course identifier. You can follow your progress with the progress bar on the right. The estimated time to complete the course is 2-3 days. In the certificate we recommend granting 1 credit (ECTS) for the course.  

    In practical matters, please contact event-support (at), and in content related questions, chipster (at) You can also join the Weekly CSC research user meetings in Zoom to discuss course matters and get help with the exercises.

12. Test your skills

  • 12. Test your skills

    "Examples are always easier" 

    Now it's time to test what you have learned and analyse your own data! Try analysing some of your own data in Chipster, find some interesting data online, or try starting from the digital expression matrix that is the end result (the digital_expression.tsv file) in example session course_single_cell_RNAseq_DropSeq_done. Don't be scared to face some new issues and try different things -this is an effective way to learn! 

    "Beginning is always the most difficult step" 

    Anyone who has analysed some data will tell you the same: cleaning and tuning your data in the very beginning is the most time consuming step. So don't get frustrated! We try to make it as easy as possible, but it's good to practise this as well, as this is often a step that is skipped in course sessions.
    • Read carefully the manual page for the Setup toolNote that if you find some data for example from GEO, it is likely in some matrix format, and will work as a digital expression matrix -note however, that there shouldn't be any extra columns, rows etc, and that it should be in the tab separated (.tsv) format. 
    • Don't be shy to ask! You can send a support request in Chipster (recommended) or send e-mail to chipster (at)
    Your final course task

    After toying around with your data, we would like to see what you came up with! In very free format, please share some of your reports, a print screen of your sessions workflow (keeping in mind that others can see what you are sharing, so no sensitive data obviously), and discuss the following questions:

    • What was different compared to the exercises?
    • What kind of decisions you had to make? 
    • Was there something you were not able to do? 
    • Any error messages you managed to tackle?