• 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 about normalising the data.

    In this section, you will learn:

    -Why do we need to normalize gene expression values
    -What is a dropout
    -What does global scaling normalization do and when does it not work well

    First, watch the lecture video:

     

     

    After watching the video, contemplate on the questions above. We will perform normalization in Chipster in section 6.

    Finally, answer the quiz questions below:

3. Set up a Seurat object and check the quality of cells5. Identify highly variable genes