Workshop Schedule

Pre-reading

Day 1

Time Topic Instructor
10:00 - 10:15 Workshop introduction Will
10:15 - 10:45 Pre-reading review and Q&A All
10:45 - 10:50 Break
10:50 - 11:25 Project setup and data exploration Will
11:25 - 11:55 Differential expression analysis using FindMarkers() Noor
11:55 - 12:00 Overview of self-learning materials and homework submission Will

Before the next class:

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

  1. Differential expression analysis visualization from FindMarkers()
    Click here for a preview of this lesson
    Visualization of the differential expression data from FindMarkers() can give important insights into our biological question of interest.

    In this lesson you will:
    - Create visualizations for differentially expressed genes
    - Discuss other statistical tests for differential expression analysis

  2. 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.

    In this lesson you will:
    - Derive the covariance matrix used for Principal Components Analysis

    - Explain the roles of eigenvectors and eigenvalues within a Principal Components Analysis
    - Compare and contrast our Principal Components Analysis to the output of prcomp()

  1. 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 running code in the lesson, email us

Day 2

Time Topic Instructor
10:00 - 10:30 Self-learning lessons discussion All
10:30 - 11:15 Aggregating counts by celltype using pseudobulk approach Noor
11:15 - 11:20 Break
11:20 - 12:00 DE analysis of pseudobulk data using DESeq2 Will

Before the next class:

There is no self-learning for Day 2.

Questions?

  • If you have any questions from today, email us

Day 3

Time Topic Instructor
10:00 - 10:15 Self-learning lessons discussion All
10:15 - 10:55 Visualization of differentially expressed genes Will
10:55 - 11:00 Break
11:00 - 11:55 Comparison of results from different DE approaches Noor
11:55 - 12:00 Overview of self-learning materials and homework submission Will

Before the next class:

I. Please study the contents and work through all the code within the following lessons: 1. Functional Analysis
Click here for a preview of this lesson
Now that we have significant genes, let’s gain some biological insight

In this lesson, we will:
- Discuss approaches for functional analysis
- Use clusterProfiler to run over-representation analsyis and visualize results
- Use clusterProfiler to run GSEA

  1. 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 running code in the lesson, email us

Day 4

Time Topic Instructor
10:00 - 10:30 Self-learning lessons discussion All
10:30 - 11:30 Methods for Differental Abundance Noor
11:30 - 11:35 Break
11:35 - 11:45 Discussion and Q&A All
11:45 - 12:00 Wrap-up Will

Resources

HBC Workshops

Papers