Schedule for the single-cell RNA-seq data analysis workshop
Pre-reading
Day 1
| Time | Topic | Instructor |
|---|---|---|
| 09:30 - 09:45 | Workshop introduction | Meeta |
| 09:45 - 10:35 | Introduction to Single Cell RNA-sequencing: a practical guide | Dr. Arpita Kulkarni |
| 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 | Meeta |
Before the next class:
I. Please study the contents and work through all the code within the following lessons:
- Quality control of cellranger counts
Click here for a preview of this lesson
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
- Quality control with additional metrics
Click here for a preview of this lesson
In addition to the QC generated by cellranger, we can also compute some of our own metrics based on the raw data we have loaded into our Seurat object.
In this lesson you will:
- Compute essential QC metrics for each sample
- Create plots to visualize metrics across cells per sample
- Critically evaluate each plot and learn what each QC metric means
- 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.
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.
Questions?
- If you get stuck due to an error while runnning code in the lesson, email us
Day 2
| Time | Topic | Instructor |
|---|---|---|
| 09:30 - 10:15 | Self-learning lessons discussion | All |
| 10:15 - 11:15 | Normalization and regressing out unwanted variation | Noor |
| 11:15 - 11:25 | Break | |
| 11:25 - 12:00 | A brief introduction to Integration | Meeta |
Before the next class:
I. Please study the contents and work through all the code within the following lessons:
- Running CCA integration and complex integration tasks
Click here for a preview of this lesson
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)
- Clustering
Click here for a preview of this lesson
From the UMAP visualization of our data we can see that the cells are positioned into groups. Our next task is to isolate clusters of cells that are most similar to one another based on gene expression.
In this lesson you will:
- Learn the theory behind clustering and how it is performed in Seurat
- Cluster cells and visualize them on the UMAP
- Clustering quality control
Click here for a preview of this lesson
After separating cells into clusters, it is crtical to evaluate whether they are biologically meaningful or not. At this point we can also decide if we need to re-cluster and/or potentialy go back to a previous QC step.
In this lesson you will:
- Check to see that clusters are not influenced by uninteresting sources of variation
- Check to see whether the major principal components are driving the different clusters
- Explore the cell type identities by looking at the expression for known markers across the clusters.
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.
Questions?
- If you get stuck due to an error while runnning code in the lesson, email us
Day 3
| 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 | Meeta |
| 11:30 - 11:45 | Seurat Cheatsheet Overview and Final Q & A | All |
| 11:45- 12:00 | Wrap up | Meeta |
Answer Keys
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
- Seurat vignettes
- Seurat cheatsheet
- Satija Lab: Single Cell Genomics Day
- Additional information about cell cycle scoring
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