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 Carpentries-style 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), published in 2020, 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.
- How to interoperate with other popular single-cell analysis ecosystems.
The focus of this tutorial is on single-cell analysis with R packages from the Bioconductor repository. Bioconductor packages are collaboratively developed by an international community of developers that agree on data and software standards to promote interoperability between packages, extensibility of analysis workflows, and reproducibility of published research.
Other popular tools for single-cell analysis include:
- Seurat, a stand-alone R package that has pioneered elementary steps of typical single-cell analysis workflows, and
- scverse, a collection of Python packages for single-cell omics data analysis including scanpy and scvi-tools.
Tutorials for working with these tools are available elsewhere and are not covered
in this tutorial. A demonstration of how to interoperate with Seurat and packages
from the scverse is given in Session 5
of this tutorial.
Other Carpentries-style tutorials for single-cell analysis with a different scope include:
- a community-developed lesson
that makes use of command-line utilities and
scanpyfor basic preprocessing steps, - and a tutorial proposal
based on
Seurat.
This lesson uses The Carpentries Workbench and is based on materials from the OSCA tutorial at the ISMB 2023.
Amezquita RA, Lun ATL, Becht E, Carey VJ, Carpp LN, Geistlinger L, Marini F, Rue-Albrecht K, Risso D, Soneson C, Waldron L, Pagès H, Smith ML, Huber W, Morgan M, Gottardo R, Hicks SC. Orchestrating single-cell analysis with Bioconductor. Nature Methods, 2020. doi: 10.1038/s41592-019-0654-x