-
Notifications
You must be signed in to change notification settings - Fork 192
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
1 changed file
with
181 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,181 @@ | ||
| # Schedule for the single-cell RNA-seq data analysis workshop | ||
|
|
||
| ## Pre-reading | ||
|
|
||
| * [Introduction to scRNA-seq](../lessons/01_intro_to_scRNA-seq.md) | ||
| * [Raw data to count matrix](../lessons/02_SC_generation_of_count_matrix.md) | ||
| * [Download this project](https://www.dropbox.com/s/vop78wq76h02a2f/single_cell_rnaseq.zip?dl=1) | ||
|
|
||
| ## Day 1 | ||
|
|
||
| | Time | Topic | Instructor | | ||
| |:-----------:|:----------:|:--------:| | ||
| | 09:30 - 09:45 | [Workshop introduction](../slides/Intro_to_workshop_all_in_person.pdf) | Noor | | ||
| | 9:45 - 10:15 | scRNA-seq pre-reading discussion | All | | ||
| | 10:15 - 10:50 | [Quality control set-up](../lessons/03_SC_quality_control-setup.md) | Meeta | | ||
| | 10:50 - 11:00 | Break | | ||
| | 11:00 - 11:40 | [Quality control: Evaluating metrics](../lessons/04_SC_quality_control.md) | Noor | | ||
| | 11:40 - 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: | ||
| 1. [Quality control of cellranger counts](../lessons/04_cellranger_QC.md) | ||
| <details> | ||
| <summary><i>Click here for a preview of this lesson</i></summary> | ||
| <br>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. <br><br>In this lesson you will:<br> | ||
| - Discuss the outputs of cellranger and how to run it <br> | ||
| - Review web summary HTML report<br> | ||
| - Create plots from metrics_summary.csv file <br><br> | ||
| </details> | ||
|
|
||
| 2. [Theory of PCA](../lessons/05_theory_of_PCA.md) | ||
| <details> | ||
| <summary><i>Click here for a preview of this lesson</i></summary> | ||
| <br>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.<br><br> | ||
| </details> | ||
| 3. [Normalization and regressing out unwanted variation](../lessons/06_SC_SCT_normalization.md) | ||
| <details> | ||
| <summary><i>Click here for a preview of this lesson</i></summary> | ||
| <br>During the analysis we will be making lots of comparisons; between cells, between samples, or both. To make accurate comparisons of gene expression we need to first perform normalization. We also want to make sure that the differences we find are a true biolgical effect and not a result of other sources of unwanted variation . <br><br>In this lesson you will:<br> | ||
| - Assess your data for any unwanted variation<br> | ||
| - Normalize the data while also regressing out any identified sources of unwanted variation <br><br> | ||
| </details> | ||
|
|
||
| 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](https://docs.google.com/forms/d/e/1FAIpQLSf4zw37eUcuNCmHxCLikg3JENoxGWIuv5c_5pqHdYNId81Arg/viewform) on **the day *before* the next class**. | ||
|
|
||
| III. **Run the code in this [script](https://github.com/hbctraining/scRNA-seq_online/raw/master/scripts/integration_code.R)** to perform the steps of integration. We will discuss the code and theory in class. | ||
|
|
||
| ### Questions? | ||
| * ***If you get stuck due to an error*** while runnning code in the lesson, [email us](mailto:hbctraining@hsph.harvard.edu) | ||
| * Post any **conceptual questions** that you would like to have **reviewed in class** at the end of the homework Google form. | ||
|
|
||
| *** | ||
|
|
||
| ## Day 2 | ||
|
|
||
| | Time | Topic | Instructor | | ||
| |:-----------:|:----------:|:--------:| | ||
| | 09:30 - 10:30| [Introduction to Single Cell RNA-sequencing: a practical guide]() | [Dr. Arpita Kulkarni](https://singlecellcore.hms.harvard.edu/people/arpita-kulkarni-phd) | | ||
| | 10:30 - 10:40 | Break | | ||
| | 10:40 - 11:00 | Self-learning lessons discussion | All | | ||
| | 11:00 - 11:30| [A brief introduction to Integration](../lessons/06a_integration_cca_theory.md) | Meeta | | ||
| | 11:30 - 12:00| [Running CCA integration and complex integration tasks](../lessons/06b_integration_code_harmony.md) | Noor| | ||
|
|
||
|
|
||
| ### Before the next class: | ||
| I. Please **study the contents** and **work through all the code** within the following lessons: | ||
|
|
||
| 1. [Running CCA integration and complex integration tasks](../lessons/06b_integration_code_harmony.md) | ||
| <details> | ||
| <summary><i>Click here for a preview of this lesson</i></summary> | ||
| <br>In class, we described the theory of integration and in what situations we would implement it. <br><br>In this lesson you will:<br> | ||
| - Run the code to implement CCA integration <br> | ||
| - Evaluate the effect of integration on the UMAP <br> | ||
| - Learn about methods for complex integration tasks (Harmonizing samples) <br> | ||
| </details> | ||
|
|
||
| 2. [Clustering](../lessons/07_SC_clustering_cells_SCT.md) | ||
| <details> | ||
| <summary><i>Click here for a preview of this lesson</i></summary> | ||
| <br>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. <br><br>In this lesson you will:<br> | ||
| - Learn the theory behind clustering and how it is performed in Seurat<br> | ||
| - Cluster cells and visualize them on the UMAP<br> | ||
| </details> | ||
|
|
||
| 3. [Clustering quality control](../lessons/08_SC_clustering_quality_control.md) | ||
| <details> | ||
| <summary><i>Click here for a preview of this lesson</i></summary> | ||
| <br>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. | ||
| <br><br>In this lesson you will:<br> | ||
| - Check to see that clusters are not influenced by uninteresting sources of variation<br> | ||
| - Check to see whether the major principal components are driving the different clusters<br> | ||
| - Explore the cell type identities by looking at the expression for known markers across the clusters.<br> | ||
| </details> | ||
|
|
||
|
|
||
| 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](https://forms.gle/Znjefroiy5joVCyR8) on **the day *before* the next class**. | ||
|
|
||
| ### Questions? | ||
| * ***If you get stuck due to an error*** while runnning code in the lesson, [email us](mailto:hbctraining@hsph.harvard.edu) | ||
| * Post any **conceptual questions** that you would like to have **reviewed in class** at the end of the homework Google form. | ||
|
|
||
| *** | ||
|
|
||
|
|
||
| ## Day 3 | ||
|
|
||
| | Time | Topic | Instructor | | ||
| |:-----------:|:----------:|:--------:| | ||
| | 9:30 - 10:00 | Self-learning lessons discussion | All | | ||
| | 10:00 - 11:00 |[Marker identification](../lessons/09_merged_SC_marker_identification.md) | Noor | | ||
| | 11:00 - 11:10 | Break | | ||
| | 11:10 - 11:30 | [Workflow summary](../lessons/scRNAseq_workflow.md) | Meeta | | ||
| | 11:30 - 11:45 | [Seurat Cheatsheet](../lessons/seurat_cheatsheet.md) Overview and Final Q & A | All | | ||
| | 11:45- 12:00 | [Wrap up](../slides/Workshop_wrapup.pdf) | Meeta | | ||
|
|
||
| *** | ||
|
|
||
| ## Answer Keys | ||
|
|
||
|
|
||
| ## Downstream analyses | ||
|
|
||
| [Differential expression between conditions](../lessons/pseudobulk_DESeq2_scrnaseq.md) | ||
|
|
||
| *** | ||
|
|
||
| ## 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](https://satijalab.org/seurat/vignettes.html) | ||
| * [Seurat cheatsheet](https://satijalab.org/seurat/essential_commands.html) | ||
| * [Satija Lab: Single Cell Genomics Day](https://satijalab.org/scgd21/) | ||
| * [Additional information about cell cycle scoring](../lessons/cell_cycle_scoring.md) | ||
|
|
||
| ### Scaling up: scRNA-seq analysis on HPC | ||
| * Using RStudio on O2 | ||
| * [HMSRC wiki page](https://harvardmed.atlassian.net/wiki/spaces/O2/pages/1623425967/RStudio+on+O2) | ||
| * [HBC RStudio on O2 tutorial](https://hbctraining.github.io/Intro-to-Unix-QMB/lessons/R_studio_on_02.html) | ||
|
|
||
| ### Cell type annotation | ||
| - Databases with markers for manual annotation | ||
| - [CellMarker 2.0](http://bio-bigdata.hrbmu.edu.cn/CellMarker/) | ||
| - Cell type signature gene sets from [MSigDb](https://www.gsea-msigdb.org/gsea/msigdb/human/genesets.jsp?collection=C8) | ||
| - [CELL x GENE from CZI](https://cellxgene.cziscience.com/gene-expression) | ||
| - Reference-based automated celltype annotation | ||
| - [Azimuth](https://azimuth.hubmapconsortium.org/) | ||
| - [Celltypist](https://www.celltypist.org/) | ||
|
|
||
|
|
||
| ### Highlighted papers | ||
|
|
||
| - ["Sampling time-dependent artifacts in single-cell genomics studies."](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02032-0) *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."](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1830-0) *O'Flanagan et al.* 2020 | ||
| - ["Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows."](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02048-6) *Denisenko et al.* 2020 | ||
| - ["Confronting false discoveries in single-cell differential expression", _Nature Communications_ 2021](https://www.nature.com/articles/s41467-021-25960-2) | ||
| - [Single-nucleus and single-cell transcriptomes compared in matched cortical cell types](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6306246/) | ||
| - [A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors](https://www.nature.com/articles/s41591-020-0844-1) | ||
| - [Ligand-receptor analysis with CellphoneDB](https://www.nature.com/articles/s41576-020-00292-x) | ||
| - [Best practices for single-cell analysis across modalities](https://www.nature.com/articles/s41576-023-00586-w) | ||
|
|
||
|
|
||
|
|
||
| ### Other online scRNA-seq courses: | ||
| - [OSCA with Bioconductor](http://bioconductor.org/books/release/OSCA/) | ||
| - [DFCI/Shirley Liu](https://liulab-dfci.github.io/bioinfo-combio/) | ||
| - [Wellcome Sanger Institute/Hemmberg Lab](https://www.singlecellcourse.org/) | ||
| - [ISCB Workshop](https://github.com/SingleCellTranscriptomics) | ||
| - [Broad workshop](https://broadinstitute.github.io/2020_scWorkshop/) | ||
| - [SciLifeLab workshop](https://nbisweden.github.io/workshop-scRNAseq/) | ||
|
|
||
|
|