Section

  • 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.
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  • View only 'Topic 1'

    1. Introduction to Chipster

    In this section, you will learn how to use the Chipster software, and where to find support and more information.

    Chipster is an easy-to-use graphical analysis software for high-throughput data such as RNA-seq and single cell RNA-seq. Chipster contains over 450 analysis tools and a collection of reference genomes. Chipster runs on your web browser, and the actual analysis jobs use CSC's powerful cloud environment.

    You don't need to know about command line usage, R or Python to get started, and any laptop with a browser and decent internet connection will do. So to get started, all you need are credentials! If you are working, studying or otherwise affiliated to a Finnish university or research institute, you can log in to Chipster with your Haka or Virtu account, or request a CSC user account. If this is not the case, you can ask for a 3-week evaluation account or purchase a one-year user account to CSC's Chipster server. The Chipster server is also available for download as a virtual machine image free of charge. More information about getting Chipster user account.

    First, watch the Chipster 101 video and check the Chipster quick tour below

    (please give some time for the video to load in order to have a sharp image).


    Make sure that you have Chipster credentials to do the exercises.

    You can log in to Chipster with your Haka or Virtu account, or with your CSC account. You can also request a 3-week evaluation account.

    Next, please go through these exercises:

    1. Open Chipster: Go to https://chipster.csc.fi/, click on Launch Chipster (use the web Chipster v4) and log in.
    2. Open training sessionClick Sessions and select the session course_single_cell_RNAseq_Seurat under Training sessions.


    Finally, answer the quiz and question below.
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    2. Introduction to scRNA-seq

    This section provides an introduction to single cell RNA-seq data analysis.

    You will learn

    • How does scRNA-seq work and what can go wrong
    • What is a UMI and why do we use them
    • Vocabulary: what are empties, doublets and dropouts
    • Why is scRNA-seq data challenging to analyze
    • What are the main analysis steps for clustering cells and finding cluster marker genes
    • What is Seurat

    First, watch the lecture video

    .

    If you have time, you can also watch a more advanced lecture by Jules Gilet (slides):

     

     

    Next, please complete these tasks:

    Task 1: We are using Seurat tools: check the Seurat webpage and the tutorials there.

    Task 2: What kind of scRNA-seq data do you have (10X Genomics, DropSeq, ...)? How is it produced (Google/ask)?

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    3. Set up a Seurat object and check the quality of cells

    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:

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    4. Normalize gene expression values

    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:

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    5. Identify highly variable genes

    In this section, we learn about the variance in the data and how to find the highly variable genes.

    In this section, you will learn

    • Why do we need to find highly variable genes
    • What kind of mean-variance relationship is there in scRNA-seq data
    • Why do we need to stabilize the variance of gene expression values

    First, watch the lecture video:

    After watching the video, contemplate on the questions above. We will perform the detection of highly variable genes in Chipster in section 6.

    Finally, answer the quiz questions below:

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    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 in the data.

    In this section, 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 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:
     
     

    Finally, answer the quiz questions below:

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    7. Dimension reduction

    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:

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    8. Cluster cells and detect marker genes for clusters

    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.

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    9. Comparing two samples

    In this session we demonstrate the use of Seurat tools for joint analysis of two samples. The session uses the same data and follows the steps in Seurat tutorial for integrated analysis.

    We begin with two expression matrixes: one for control PBMC cells, and another for PBMC cells stimulated with interferon beta. So now the cells will cluster based on cell type, but also based on the treatment, which makes the analysis a bit more complex. 

    We wish to: 

    • Identify cell types that are present in both datasets 
    • Obtain cell type markers that are conserved in both control and stimulated cells 
    • Compare the datasets to find cell-type specific responses to stimulation

    The first steps of the analysis are already familiar to us. After we have preprocessed both samples, we combine them, perform the integrated analysis, find markers for samples and for clusters, and visualise these.

    The process of integrating samples is described in more detail in Methods section in the paper by Stuart*, Butler*, et al., Cell 2019. (You can access the paper in bioRxiv)

    Please watch the video for introduction to two sample analysis:

      

     

    We are switching to another dataset and another Chipster session, with two samples.

    Please go through these exercises:

    1. Open example session
    Click Sessions and open training session course_single_cell_RNAseq_Seurat_integrated.

    2. Setup Seurat object & quality control
    Select the immune_control_expression_matrix.txt.gz. Select tool Single cell RNA-seq / Seurat -Setup and QC. Check the parameters, and name your sample as CTRL. You can name the project and give a bit stricter parameter for filtering (genes expressed at least in 5 cells for example). Make sure that you have assigned the file correctly: this is a digital expression matrix (a DGE table). Run the tool.
    Repeat this step for the immune_stimulated_expression_matrix.txt.gz, except now name the sample as STIM.

    3. Filtering, regression and detection of variable gene
    Select seurat_obj.Robj. Select the tool Single cell RNA-seq / Seurat - Filter cells, normalize, regress and detect variable genes. Adjust the parameters so that you are filtering out cells that have less than 500 genes expressed, and run the tool. Repeat for the other sample as well.
    Once the tool is done, open the Dispersion_plot.pdf and check also the second page.
    How many variable genes are there?

    4. Combine two samples

    Select both seurat_obj.R objects from the previous step and run the tool Single cell RNA-seq / Seurat –Combine two samples.


    Please watch the video for aligning two samples and clustering:

     

     

    For more theory, you can view the lecture video by Ahmed Mahfouz (slides):

     

    Then go through these exercises:

    5. Integrated analysis of two samples

    Select the combined seurat_obj.Robj from the previous step. Run the tool Single cell RNA-seq / Seurat –Integrated analysis of two samples with default parameters.

    While waiting, you can study the manual (click More info...). What are the main steps of this tool?

    When the results are ready, study the integrated_plot.pdfHow many clusters are there in this data?


    Please watch the video for finding differentially expressed genes and conserved cluster markers:

      

     

    Then go through these exercises:

    6. Find conserved cluster markers and DE genes in two samples
    Select the seurat_obj.Robj from the previous step. Run tool Find conserved cluster markers and DE genes in two samples for a cluster of your interest. Inspect the tables generated by the tool.
    What was used as a cut-off for the adjusted p-value?
    How many differentially expressed genes were there between the two samples in this cluster? Write down few interesting genes from the list for the visualization exercise 7.
    How many conserved biomarkers were recognized for the cluster? Write down few interesting genes from the list for the next tool.

    7. Visualize markers and differentially expressed genes
    Choose seurat_obj.Robj generated in step 5. Select tool Single cell RNA-seq / Seurat - Visualize genes with cell type specific responses in two samples. Type the gene names to the parameter field (the ones you listed in previous step, or try for example: CD3D, GNLY, IFI16, ISG15, CD14, CXCL10). Use comma (,) as a separator. You can run the tool several times for different gene lists.
    Open split_dot_plot.pdf.
    Are the differentially expressed genes expressed differently also in other clusters? Are the conserved markers expressed in other clusters?

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    10. Test your skills

    "Examples are always easier" 

    Now it's time to test what you have learned and analyse your own data! Try analysing some of your own data in Chipster, find some interesting data online, or try starting from the digital expression matrix that is the end result (the 

    digital_expression.tsv file) in example session course_single_cell_RNAseq_DropSeq_done. Don't be scared to face some new issues and try different things -this is an effective way to learn!

    "Beginning is always the most difficult step" 

    Anyone who has analysed some data will tell you the same: cleaning and tuning your data in the very beginning is the most time consuming step. So don't get frustrated! We try to make it as easy as possible, but it's good to practise this as well, as this is often a step that is skipped in course sessions.
    • Read carefully the manual page for the Setup tool 
    • Don't be shy to ask! You can send a support request in Chipster (recommended) or send e-mail to chipster (at) csc.fi
    Your final course task

    After toying around with your data, we would like to see what you came up with! In very free format, please share some of your reports, a print screen of your sessions workflow (keeping in mind that others can see what you are sharing, so no sensitive data obviously), and discuss the following questions:

    • What was different compared to the exercises?
    • What kind of decisions you had to make? 
    • Was there something you were not able to do? 
    • Any error messages you managed to tackle?

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    Course certificate and feedback

    Congratulations on completing the course! 

    You can download the course certificate from the link below, after you have finished all the sections, including giving course feedback, and the tasks/quizzes.

    We are actively developing both the course contents as well as Chipster software, so please 

    • give us feedback (you can find the link below) 
    • write your thoughts on Chipster in the forum below.

    Need help?

    • If you have any questions or you need support with Chipster, feel free to e-mail us at chipster@csc.fi
    • Practical/technical questions regarding the course can be sent to event-support@csc.fi

    Accessing the course materials after the course?