The cytoverse is a collection of open source R based tools developed by the founders of Ozette Technologies which facilitate programmatic analysis of flow cytometry data.
A number of packages make up the cytoverse. flowCore, flowWorkspace, openCyto, and ggcyto make up the core of the cytoverse. These packages are, by and large, powerful and sufficient to create robust and reproducible analysis workflows for flow cytometry data. Additional available packages such as flowClust, flowStats, CytoQC, or CytoML can be further utilized for niche applications including, model based clustering of flow cytometry data, QC and standardization of set of FCS files, even parsing of workspaces from FlowJo or cytobank.
This website contains training materials that will be presented at a workshop at Bioc2023, on August 2nd. During Bioc2023, you can launch an instance of rstudio server containing these materials by navigating to workshop.bioconductor.org. If you couldn't attend the conference, don't worry, as we'll add links to videos of the workshop. You may also explore these training materials at your own pace.
The aim of this workshop is to empower flow cytometry users and analysts towards reproducible and programmatic analysis.
By the end of this workshop, the attendees will be able to
- Import flow cytometry data
- Understand the difference between uncompensated, compensated, and transformed data,
- Identify sub-populations by manual or semi-automated gating of markers,
- Access and extract expression matrix from a gated data,
- Be aware of csv-templating of Gating to perform large-scale gating
- Identify and generate important plots to assess the data and quality,
- Generate plots summarizing the expression of markers and abundance of various sub-populations
| Activity | Time |
|---|---|
| Introduction and use of docker container | 5 minutes |
| Basics of working with FCS files | 30 minutes |
| Spillover, Transformation | 25 minutes |
Gating Cells in the cytoverse |
40 minutes |
Visualization using ggcyto |
on your own |
| Reporting | on your own |
| Wrap-up | 10 minutes |
We'd love to know more about the open source community so that we can better support their needs. Please complete our survey.
- Some R knowledge,
- Basic flow cytometry knowledge,
- Willingness to ask questions and learn
For this workshop, we will use subset of a public data set: FR-FCM-Z5PC that can be found in flowrepository.org. The dataset was published in the following paper.
What do these various packages do, and where should you look for a piece of functionality?
- flowWorkspace: provides core data structures and methods for use in cytometry data analysis, including compensation, transformation, and gating.
- flowCore: provides additional data structures and methods for use in cytometry data analysis, however in some cases has been superceded by
flowWorkspace - ggcyto: make plots of distributions of cells and their gates using a ggplot2-based framework
- CytoML: import and export of cytometry data to or from FlowJo, BD FACSDIVA, and Cytobank workspace formats.
- CytoQC: wrangle and standardize collections of FCS files.
- openCyto: openCyto enables the development of reproducible automated analysis pipelines
- flowClust: implements various unsupervised clustering methods
- flowStats: enables calculation of sample-level summaries of gated populations
You can get an instance of rstudio server that contains these materials, and all of their dependencies by installing docker, and then running
docker run -network=host -e PASSWORD=<choose_a_password_for_rstudio> -p 8787:8787 ghcr.io/ozettetech/cytoverse-bioc-2023:latest