• 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:


    Practicalities
    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, you will learn about dimension reduction (PCA, tSNE, UMAP) and selecting the principal components for the clustering step.

    The data has multiple dimensions, as there can be thousands of cells and thousands of genes. This would make the data tricky to deal with, which is why dimension reduction step is needed. We reduce the dimensions to ease the clustering step, and also to make the visualisation of the data possible.

    First, watch the theory lectures about dimensional reduction:

       

        

    For more theory, you can view the lecture by Paulo Czarnewski (slides):

      

     

      

    Next, do the following exercises in Chipster:

    Principal component analysis
    Select seurat_obj.Robj from the previous step and run the tool Single cell RNA-seq / Seurat -PCA. Open PCAplots.pdf. Look at the heatmaps and the standard deviation of PCs in the last two pages.

    How many principal components should we continue the analysis with (check the elbow in the standard deviation plot, inspect the heatmaps)? Would 10 be ok?


    Finally, answer the questions in the quiz:
6. Scale data and regress out unwanted sources of variation8. Cluster cells and detect marker genes for clusters