Introduction to single-cell RNA-seq

Learning Objectives

• Understand the considerations when designing a single-cell RNA-seq experiment
• Discuss the steps involved in taking raw single-cell RNA-sequencing data and generating a count (gene expression) matrix
• Compute and assess QC metrics at every step in the workflow
• Cluster cells based on expression data and derive the identity of the different cell types present
• Perform integration of different sample conditions

Installations

1. Follow the instructions linked here to download R and RStudio + Install Packages from CRAN and Bioconductor

2. Download this project

Lessons

Part 1

1. Introduction to scRNA-seq
2. Raw data to count matrix

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Part II

1. Quality control set-up
2. Quality control of CellRanger counts
3. Quality control with additional metrics
4. Theory of PCA

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Part III

1. Normalization and regressing out unwanted variation
2. A brief introduction to Integration
3. Running CCA integration and complex integration tasks
4. Clustering
5. Clustering quality control
6. Seurat Cheatsheet
7. Marker identification
8. Workflow summary

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Building on this workshop

• Downstream analysis
  • Differential expression between conditions
• Other online scRNA-seq courses:
  • http://bioconductor.org/books/release/OSCA/
  • https://liulab-dfci.github.io/bioinfo-combio/
  • https://hemberg-lab.github.io/scRNA.seq.course/
  • https://github.com/SingleCellTranscriptomics
  • https://broadinstitute.github.io/2020_scWorkshop/
• Resources for scRNA-seq Sample Prep:
  • https://www.protocols.io/
  • https://support.10xgenomics.com/single-cell-gene-expression/sample-prep
  • https://community.10xgenomics.com/

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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 vignettes
• Seurat cheatsheet
• Satija Lab: Single Cell Genomics Day
• “Principal Component Analysis (PCA) clearly explained”, a video from Josh Starmer
• Additional information about cell cycle scoring
• Using R on the O2 cluster
• Highlighted papers for sample processing steps (pre-sequencing):
  • “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
• Best practices for single-cell analysis across modalities