• 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 clustering of the cells, and finding and visualising cluster marker genes.

    We want to know what kind of cells are present in our dataset, so we cluster the cells, and study the similarities in expression within these clusters. Clustering is not a simple task! Luckily the Seurat tools wrapped in the Chipster's clustering tool will take care of it, but it is good to understand what happens under the hood.

    First, watch the lecture video about clustering:

     

     

    For more theory, you can also watch the lecture by Ahmed Mahfouz (slides):

     

     

    Once we have clustered the cells, we can look for marker genes for these clusters. In this section you will learn

    -What is a marker gene
    -What aspects of scRNA-seq data complicate differential expression analysis
    -Why do we want to filter out genes prior to statistical testing

    Watch the lecture on detecting marker genes for clusters


    After watching the video, contemplate on the questions above.

    Next, do the following exercises in Chipster:

    1. Clustering
    Select seurat_obj.Robj from the previous step. Select tool Single cell RNA-seq / Seurat - Clustering and detection of cluster marker genes. In the parameters, set Number of principal components to use = 10.
    While waiting for the tool to run, you can study the manual (click "More info..." to access the manual page).

    What are the three main steps of this tool?

    When the results are ready, inspect the clusterPlot.pdf. How many clusters are there in this data?

    2. Marker genes for clusters

    Check in the parameters of the previously run tool what is the default statistical for detecting marker genes for clusters? Repeat the run so that you set Which test to use for finding marker genes = MAST.
    Filter the result lists of both Wilcoxon and MAST tests by adjusted p-value: Select both markers.tsv files and run Utilities / Filter table by column value using the following parameters:
    Column to filter by = p_val_adj
    Does the first column have a title = no
    Cutoff = 0.05
    Filtering criteria = smaller-than

    Open both filtered-ngs-results.tsv files as spreadsheet and note that they contain marker genes for all the clusters. Check how many marker genes were found by each test. What gene is the most specific marker for the first cluster (cluster 0)?

    3. Marker genes for a specific cluster

    Let's retrieve marker genes for cluster 3. Select both filtered-ngs-results.tsv files and run Utilities / Filter table by column value using the following parameters:
    Column to filter by = cluster
    Does the first column have a title = no
    Cutoff = 3
    Filtering criteria = equal-to

    Compare the marker genes found by Wilcoxon test and MAST: Rename the result files as wilcox.tsv and mast.tsv. Select both renamed files and the interactive visualization Venn diagram.
    How many marker genes were found for cluster 3 by both methods? Make a new file containing only those genes: Click on the intersect area and click Create file.

    4. Visualize markers

    Choose seurat_obj.Robj generated in the clustering step. Select tool Single cell RNA-seq / Seurat -Visualize genes. Type marker gene names in the parameter field (try for example MS4A1, LYZ, PF4).
    Open the biomarker_plot.pdf.

    Is your gene a good marker for cluster 3?

    Finally, answer the questions in the quiz.

7. Dimension reduction9. Comparing two samples