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:
- Differential expression analysis visualization from
FindMarkers()
Click here for a preview of this lesson
Visualization of the differential expression data fromFindMarkers()
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
- 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 ofprcomp()
- 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 AnalysisClick 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
- 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
- Introduction to scRNA
- Introduction to Differential Gene Expression Analysis for bulkRNA datasets
Papers
- “Confronting false discoveries in single-cell differential expression” Squair et al. 2021
- “Benchmarking integration of single-cell differential expression” Nguyen et al. 2023