- Dario Righelli, University of Padova, Italy
- Marcel Ramos, CUNY Graduate School of Public Health and Health Policy; and Roswell Park Comprehensive Cancer Center, United States
- Ludwig Geistlinger, Harvard Medical School, United States
- Davide Risso, University of Padova, Italy
In the last few years, the profiling of a large number of genome-wide features in individual cells has become routine. Consequently, a plethora of tools for the analysis of single-cell data has been developed, making it hard to understand the critical steps in the analysis workflow and the best methods for each objective of one’s study.
This tutorial aims to provide a solid foundation in using Bioconductor tools for single-cell RNA-seq analysis by walking through various steps of typical workflows using example datasets.
This tutorial uses as a "text-book" the online book "Orchestrating Single-Cell Analysis with Bioconductor" (OSCA), started in 2018 and continuously updated by many contributors from the Bioconductor community. Like the book, this tutorial strives to be of interest to the experimental biologists wanting to analyze their data and to the bioinformaticians approaching single-cell data.
Attendees will learn how to analyze multi-condition single-cell RNA-seq from raw data to statistical analyses and result interpretation. Students will learn where the critical steps and methods choices are and will be able to leverage large-data resources to analyze datasets comprising millions of cells.
In particular, participants will learn:
- How to access publicly available data, such as those from the Human Cell Atlas.
- How to perform data exploration, normalization, and dimensionality reduction.
- How to identify cell types/states and marker genes.
- How to correct for batch effects and integrate multiple samples.
- How to perform differential expression and differential abundance analysis between conditions.
- How to work with large out-of-memory datasets.
| Activity | Time |
|---|---|
| Introduction and Setup | 9:00-9:30 |
| Introduction to Bioconductor and the SingleCellExperiment class | 9:30-10:00 |
| Exploratory Data Analysis and Quality Control (EDA/QC) | 10:00-10:45 |
| Coffee break | 10:45-11:00 |
| Clustering and cell type annotation | 11:00-12:00 |
| Multi-sample analyses | 12:00-13:00 |
| Lunch break | 13:00-14:00 |
| Working with large data | 14:00-15:00 |
| Accessing the Human Cell Atlas (HCA) Data from R/Bioconductor | 15:00-16:00 |
| Coffee break | 16:00-16:15 |
| Case study: from data import to DE and DA | 16:15-17:00 |
| Case study: discussion | 17:00-18:00 |
To run this tutorial in a Docker container, pull the Docker image via
docker pull ghcr.io/bioconductor/ismb.osca:latest
and then run the image via
docker run -e PASSWORD=bioc -p 8787:8787 ghcr.io/bioconductor/ismb.osca
Once running, navigate to http://localhost:8787/ in your browser and login with
username rstudio and password bioc.
This tutorial can be installed like an ordinary R package via:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!require("remotes", quietly = TRUE))
install.packages("remotes")
BiocManager::install("Bioconductor/ISMB.OSCA",
dependencies = TRUE,
build_vignettes = TRUE)