- Introduction to scRNA-seq
- scRNA-seq: From sequence reads to count matrix
- scRNA-seq: From counts to clusters
- Download this project
| Time | Topic | Instructor |
|---|---|---|
| 09:30 - 09:45 | Workshop introduction | Noor |
| 09:45 - 10:15 | Pre-reading review and Q&A | All |
| 10:15 - 10:25 | Break | |
| 10:25 - 11:00 | Project setup and data exploration | Noor |
| 11:00 - 11:50 | Differential expression analysis using FindMarkers() |
Meeta |
| 11:50 - 12:00 | Overview of self-learning materials and homework submission | Meeta |
I. Please study the contents and work through all the code within the following lessons:
- Aggregating counts by celltype using pseudobulk approach
Click here for a preview of this lesson
Forming pseudobulk samples is important to perform accurate differential expression analysis. Treating each cell as an independent replicate leads to underestimation of the variance and misleadingly small p-values. Working on the level of pseudobulk ensures reliable statistical tests because the samples correspond to the actual units of replication.
In this lesson you will:
- Aggregate counts for a given celltype
- Demonstrate an efficent way to aggregate counts for multiple celltypes
- Use the aggregated counts to create a DESeq2 object for downstream analysis
- DE analysis of pseudobulk data using DESeq2
Click here for a preview of this lesson
The next step is to take the DESeq2 object and run through the analysis workflow to identify differentially expressed genes.
In this lesson you will:
- Perform sample level QC
- Evaluate gene-wise dispersions to evalute model fit
- Extract results and understand the statistics generated
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.
- If you get stuck due to an error while running code in the lesson, email us
| Time | Topic | Instructor |
|---|---|---|
| 09:30 - 10:00 | Self-learning lessons discussion | All |
| 10:00 - 10:40 | Visualization of differentially expressed genes | Meeta |
| 10:40 - 10:50 | Break | |
| 10:50 - 12:00 | Comparison of results from different DE approaches | Noor |
I. Please study the contents and work through all the code within the following lessons:
- 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
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.
- If you get stuck due to an error while running code in the lesson, email us
| Time | Topic | Instructor |
|---|---|---|
| 09:30 - 10:15 | Self-learning lessons discussion | All |
| 10:15 - 11:15 | Methods for Differental Abundance | Noor |
| 11:15 - 11:20 | Break | |
| 11:25 - 12:00 | Discussion and Q&A | All |
| 11:45 - 12:00 | Wrap-up | Meeta |