Workshop Schedule

Pre-reading

• Introduction to scRNA-seq
• scRNA-seq: From sequence reads to count matrix
• scRNA-seq: From counts to clusters
• Download this project by left-clicking the link

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()
   
   

2. 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

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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
   
   

2. 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

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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

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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