Kurs: Single-cell RNA-seq data analysis with Chipster, Section: 6. Scale data and regress out unwanted sources of variation | csc

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  • 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 "Introduction to scRNAseq integration". 
    This course contains two types of lecture videos: short lectures on each topic by trainers from CSC (ELIXIR-FI), and more in-depth lectures by Paulo Czarnewski (NBIS / ELIXIR-SE), Ahmed Mahfouz (LUMC / ELIXIR-NL) and Jules Gilet (ELIXIR-FR).

    You will learn the following topics, and how to perform these steps in the Chipster software:
    • UMAP plot showing how cells (dots) are clusteredperform quality control and filter out low quality cells
    • normalize gene expression values (with global scaling normalization and SCTransform)
    • scale data and remove unwanted sources of variation
    • select highly variable genes
    • perform dimensionality reduction (PCA, tSNE, UMAP, CCA)
    • cluster cells
    • find marker genes for a cluster
    • annotate cells and clusters using a reference data
    • take a closer look at the Seurat objects
    • 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
    "It is so nice to be able to do the whole workflow in Chipster, compared to the old model, where I had to transfer the tsv file to R-studio and run Seurat there. -- I learned how to use the Seurat tools in Chipster and what all the steps really mean. I learned to check the results after every step to adjust the next steps parameters and to test different PCA plotting tools. I also learned how to find different genes in the clusters and how to visualize them. I never got this far using the R-pipeline. " -Pinja, course participant & PhD student from University of Helsinki

    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.      

    Help
    In practical matters, please contact event-support (at) csc.fi, and in content related questions, chipster (at) csc.fi. You can also join the Weekly CSC research user meetings in Zoom to discuss course matters and get help with the exercises.
5. Identify highly variable genes
7. Dimension reduction

6. Scale data and regress out unwanted sources of variation

  • 6. Scale data and regress out unwanted sources of variation

    In this section we learn about scaling the data and removing unwanted sources of variation.

    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 setup_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:
     
     

    You can test this option by re-running the tool also with the cell cycle stage filtering option on (for the setup_eurat_obj.R object) if you like.

    Finally, answer the quiz questions below:

5. Identify highly variable genes
7. Dimension reduction