Schedule - Introduction to Single-cell RNA-seq
Schedule
Pre-reading
Day 1
| Time | Topic | Instructor |
|---|---|---|
| 09:30 - 09:45 | Workshop introduction | Will |
| 09:45 - 10:35 | Introduction to Single Cell RNA-sequencing: a practical guide | Dr. Mandovi Chatterjee |
| 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 | Will |
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
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
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:
- Review the mathematical concepts behind PCA
- Understand how PCA reduces dimensionality in high-throughput data
- Interpret PCA plots in the context of scRNA-seq analysis
- 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 - 10:00 | Self-learning lessons discussion | All |
| 10:00 - 10:45 | Quality control with additional metrics | Noor |
| 10:45 - 10:50 | Break | |
| 10:50 - 11:40 | Normalization and regressing out unwanted variation | Noor |
| 11:40 - 12:00 | A brief introduction to Integration | Will |
Before the next class:
I. Please study the contents and work through all the code within the following lesson:
1. Running CCA integration and complex integration tasks
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)
- Submit your work:
- The 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 |
|---|---|---|
| 09:30 - 10:00 | Self-learning lessons discussion | All |
| 10:00 - 11:00 | Clustering | Noor |
| 11:00 - 11:05 | Break | |
| 11:05 - 12:00 | Clustering quality control | Noor |
Before the next class:
I. Please study the contents and work through all the code within the following lesson:
At this point, we have populated our Seurat object with many different pieces of information. Knowing how to access different values will allow you to interact more efficiently with your dataset.
In this lesson, you will:
- Explore the different parts of a Seurat object
- Use the built-in functions from the Seurat package for visualizations
- Retrieve data efficiently from the Seurat object for downstream analyses
- Submit your work:
- The 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 4
| 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 | Will |
| 11:30 - 11:45 | Overview and Final Q & A | All |
| 11:45- 12:00 | Wrap up | Will |
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
- Using RStudio on O2
Cell type annotation
- Databases with markers for manual annotation
- CellMarker 2.0
- Cell type signature gene sets from MSigDb
- CELL x GENE from CZI
- Reference-based automated celltype annotation
Highlighted papers
- “Sampling time-dependent artifacts in single-cell genomics studies.” Massoni-Badosa et al. 2019
- “Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses.” O’Flanagan et al. 2020
- “Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows.” Denisenko et al. 2020
- “Confronting false discoveries in single-cell differential expression”, Nature Communications 2021
- Single-nucleus and single-cell transcriptomes compared in matched cortical cell types
- A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors
- Ligand-receptor analysis with CellphoneDB
- Best practices for single-cell analysis across modalities