Single-cell RNA-seq data analysis workshop

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
3. Overview of Clustering Workflow
4. Theory of PCA
5. Normalization and regressing out unwanted variation

• Solution to exercises in above lessons

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Part III

1. Integration
2. Clustering
3. Clustering quality control
4. Marker identification

• Solution to exercises in above lessons

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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

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These materials have been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.