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README.md

Schedule for the single-cell RNA-seq data analysis workshop

Pre-reading

Day 1

Time Topic Instructor
09:30 - 09:45 Workshop introduction Meeta
09:45 - 10:35 Introduction to Single Cell RNA-sequencing: a practical guide Dr. Arpita Kulkarni
10:35 - 10:40 Break
10:40 - 11:00 scRNA-seq pre-reading discussion All
11:00 - 11:45 Quality control set-up Noor
11:45 - 12:00 Overview of self-learning materials and homework submission Meeta

Before the next class:

I. Please study the contents and work through all the code within the following lessons:

  1. Quality control of cellranger counts

    Click here for a preview of this lesson
    Before you start any analysis, it’s important to know whether or not you have good quality cells. At these early stages you can flag or remove samples that could produce erroneous results downstream.

    In this lesson you will:
    - Discuss the outputs of cellranger and how to run it
    - Review web summary HTML report
    - Create plots from metrics_summary.csv file

  2. Quality control with additional metrics

    Click here for a preview of this lesson
    In addition to the QC generated by cellranger, we can also compute some of our own metrics based on the raw data we have loaded into our Seurat object.

    In this lesson you will:
    - Compute essential QC metrics for each sample
    - Create plots to visualize metrics across cells per sample
    - Critically evaluate each plot and learn what each QC metric means

  3. Theory of PCA

    Click here for a preview of this lesson
    Before we can begin the next steps of the workflow, we need to make sure you have a good understanding of Principal Components Analysis (PCA). This method will be utilized in the scRNA-seq analysis workflow, and this foundation will help you better navigate those steps and interpretation of results.

II. Submit your work:

  • Each lesson above contains exercises; please go through each of them.
  • Submit your answers to the exercises using this Google form on the day before the next class.

Questions?

  • If you get stuck due to an error while runnning code in the lesson, email us

Day 2

Time Topic Instructor
09:30 - 10:15 Self-learning lessons discussion All
10:15 - 11:15 Normalization and regressing out unwanted variation Noor
11:15 - 11:25 Break
11:25 - 12:00 A brief introduction to Integration Meeta

Before the next class:

I. Please study the contents and work through all the code within the following lessons:

  1. Running CCA integration and complex integration tasks

    Click here for a preview of this lesson
    In class, we described the theory of integration and in what situations we would implement it.

    In this lesson you will:
    - Run the code to implement CCA integration
    - Evaluate the effect of integration on the UMAP
    - Learn about methods for complex integration tasks (Harmonizing samples)

  2. Clustering

    Click here for a preview of this lesson
    From the UMAP visualization of our data we can see that the cells are positioned into groups. Our next task is to isolate clusters of cells that are most similar to one another based on gene expression.

    In this lesson you will:
    - Learn the theory behind clustering and how it is performed in Seurat
    - Cluster cells and visualize them on the UMAP

  3. Clustering quality control

    Click here for a preview of this lesson
    After separating cells into clusters, it is crtical to evaluate whether they are biologically meaningful or not. At this point we can also decide if we need to re-cluster and/or potentialy go back to a previous QC step.

    In this lesson you will:
    - Check to see that clusters are not influenced by uninteresting sources of variation
    - Check to see whether the major principal components are driving the different clusters
    - Explore the cell type identities by looking at the expression for known markers across the clusters.

II. Submit your work:

  • Each lesson above contains exercises; please go through each of them.
  • Submit your answers to the exercises using this Google form on the day before the next class.

Questions?

  • If you get stuck due to an error while runnning code in the lesson, email us

Day 3

Time Topic Instructor
9:30 - 10:00 Self-learning lessons discussion All
10:00 - 11:00 Marker identification Noor
11:00 - 11:10 Break
11:10 - 11:30 Workflow summary Meeta
11:30 - 11:45 Seurat Cheatsheet Overview and Final Q & A All
11:45- 12:00 Wrap up Meeta

Answer Keys

Downstream analyses

Resources

We have covered the analysis steps in quite a bit of detail for scRNA-seq exploration of cellular heterogeneity using the Seurat package. For more information on topics covered, we encourage you to take a look at the following resources:

Seurat-focused

Scaling up: scRNA-seq analysis on HPC

Cell type annotation

Highlighted papers

Other online scRNA-seq courses: