• 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 we learn how to perform some QC and filter out cells from the input files.

    In this section, you will learn

    -What kind of input files can be used
    -What is the structure of 10X Genomics matrix file
    -How to filter out genes
    -How to check the quality of cells and filter out bad ones

    In the exercises, we start from a set of files that are the output of the 10X Genomics device. These files are converted into a Seurat object, which is passed from one tool to another: each tool adds something to the object. 

    First, watch the lecture video:  

    After watching the video, contemplate on the questions above.

     

    Next, please go through this exercise in Chipster:

    Set up a Seurat object and perform quality control

    Select the files.tar.gz. Select tool Single cell RNA-seq / Seurat -Setup and QC. Check the parameters, and name your project PBMC. Run the tool.
    Open the QCplots.pdf in new tab. Look at both pages.

    Based on the plots, what would be the optimal upper limit for the number of genes expressed and mitochondrial transcript percentage? Hint: check the default parameters used in the tool Seurat -Filter cells, normalize, regress and detect variable genes. We will perform the filtering in Chipster in section 6.

    Finally, answer the quiz questions below:

2. Introduction to scRNA-seq4. Normalize gene expression values