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Workshop website: https://phipsonlab.github.io/single_cell_workshop/

Overview

Single-cell RNA sequencing (scRNA-seq) has revolutionised our ability to study gene expression at the resolution of individual cells, enabling the discovery of novel cell types and providing insights into the cellular composition of complex tissues. This workshop provides a comprehensive introduction to the computational analysis of scRNA-seq data using R and Bioconductor.

We analyse single-nucleus RNA-sequencing (snRNA-seq) data from human heart tissue across three developmental stages: foetal, young, and adult. The dataset originates from Sim et al. (2021) examining sex-specific control of human heart maturation (Circulation).

Pre-requisites

This workshop is designed for researchers and students who:

  • Have basic familiarity with R programming (data manipulation, plotting)
  • Are interested in single-cell transcriptomics analysis
  • Want to understand best practices for scRNA-seq data processing

No prior experience with single-cell analysis or Bioconductor is required. All concepts are introduced from first principles with detailed explanations.

System Requirements

Resource Minimum Recommended
RAM 8 GB 16 GB
Disk space 5 GB free 10 GB free
R version 4.3+ 4.5.2
RStudio 2023.06+ Latest

Workshop Outline

Session 1: Core Single Cell Analysis (Morning, ~3 hours)

Module Topic Duration
Module 1 Quality Control 45 min
Break 10 min
Module 2 Normalisation & Integration 50 min
Break 10 min
Module 3 Cell Type Annotation 20 min
Module 4 Differential Expression 55 min
Wrap-up & Q&A 10 min

Session 2: Downstream Analysis (Afternoon, ~3 hours)

Coming soon - Additional downstream analyses using the same heart development dataset.

Learning Objectives

By the end of this workshop, participants will be able to:

  • Load and explore 10X Genomics scRNA-seq data in R using Seurat
  • Calculate and interpret per-cell quality control metrics
  • Apply appropriate filtering thresholds to remove low-quality cells
  • Normalise data using SCTransform and correct batch effects with Harmony
  • Perform graph-based clustering and visualise results with UMAP
  • Annotate cell types using canonical marker genes
  • Understand the pseudoreplication problem in single-cell differential expression
  • Perform statistically rigorous differential expression analysis using pseudobulk methods
  • Analyse cell type composition changes using propeller

Dataset

The workshop uses snRNA-seq data from human heart tissue (Sim et al., 2021):

Group Samples Age Range Description
Foetal 3 19-20 weeks Developing heart
Young 3 4-14 years Postnatal maturation
Adult 3 35-42 years Mature heart

Total: 9 samples, ~47,000 nuclei after quality control

Methods Covered

Analysis Step Method Package
Quality control Per-cell metrics, filtering Seurat
Normalisation SCTransform v2 Seurat, glmGamPoi
Batch correction Harmony harmony
Dimensionality reduction PCA, UMAP Seurat
Clustering Louvain algorithm Seurat
Cell type annotation Marker-based (manual) Seurat
Differential expression Pseudobulk + limma-voom edgeR, limma
Composition analysis propeller speckle

Quick Start

Please complete setup at least one day before the workshop.

  1. Clone or download this repository
  2. Open single_cell_workshop.Rproj in RStudio
  3. Follow Module 0: Setup for detailed instructions

The setup involves: - Installing packages with renv::restore() (~10-15 minutes) - Downloading data from Zenodo (~420 MB, ~5 minutes)

Key Package Versions

This workshop uses pinned package versions for reproducibility:

Package Version Package Version
R 4.5.2 Bioconductor 3.22
Seurat 5.4.0 edgeR 4.8.2
SeuratObject 5.3.0 limma 3.66.0
harmony 1.2.4 speckle 1.10.0
glmGamPoi 1.22.0

Workshop Materials

Session 1: Core Single Cell Analysis

Module Topic Description
Module 0 Setup Environment setup and data download
Module 1 Quality Control QC metrics, cell filtering
Module 2 Integration Normalisation, batch correction, clustering
Module 3 Annotation Marker genes and cell type assignment
Module 4 DE Analysis Pseudobulk DE and composition analysis

Session 2: Downstream Analysis

Coming soon - Additional downstream analyses using the same heart development dataset.

Citation

If you use materials from this workshop, please cite:

Original dataset:

Sim CB, Phipson B, Ziemann M, et al. Sex-Specific Control of Human Heart Maturation by the Progesterone Receptor. Circulation. 2021;143(10):1614-1628. doi:10.1161/CIRCULATIONAHA.120.051921

Acknowledgements

This workshop was developed by the Phipson Lab using data from the Porrello and Hewitt laboratories. We thank the original authors for making their data publicly available.

License

This project is licensed under the MIT License - see the LICENSE file for details.