• 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 "Stimulated vs Control PBMCs".

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

    • tSNE plot showing how cells (dots) are clusteredperform quality control and filter out low quality cells
    • normalize gene expression values
    • scale data and remove unwanted sources of variation
    • select hihgly 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

    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) csc.fi, and in content related questions, chipster (at) csc.fi.
  • In this section we learn about scaling the data and removing unwanted sources of variation in the data.

    In this section, you will learn

    -Why do we need to scale data prior to PCA
    -How is scaling done
    -How can we remove unwanted sources of variation

    First, watch the lecture video:

    After watching the video, contemplate on the questions above.


    Next, please go through this exercise in Chipster:

    Filter cells, normalize expression values, scale data and regress out unwanted variation, and detect variable genes

    Select seurat_obj.Robj (this is an R-object, which can be exported and opened in R, or just passed to the next tool in Chipster, like we do now). Select the tool Single cell RNA-seq / Seurat - Filter cells, normalize, regress and detect variable genes. Check if the default parameters are good for this dataset, based on the QCplots.pdf. While the tool is running, click More info... and read about the four steps this tool performs.
    Once the analysis is ready, open the Dispersion_plot.pdf and check also the second page.

    How many cells were filtered out? What are the ten most highly variable genes?

    Bonus: How to regress out variation caused by cell cycle stage? 
    Please watch the following video about regressing out the cell cycle stage:

    Finally, answer the quiz questions below:

5. Identify highly variable genes7. Dimension reduction