Abstract
Cytometry in R: A free weekly course for coding beginners
David Rach1,2, Natarajan Ayithan2, Xiaoxuan Fan2 1 Molecular Microbiology and Immunology Graduate Program, University of Maryland School of Medicine, Baltimore, USA 2 Flow Cytometry Shared Resource, University of Maryland Greenebaum Comprehensive Cancer Center, Baltimore, USA
As spectral flow cytometry panels continue to expand in complexity, comprehensive analysis of the resulting high-dimensional datasets becomes increasingly challenging. The identification of previously uncharacterized cell populations within this multidimensional space remains a significant obstacle. To address this, both semi-supervised and unsupervised analytical approaches are required to enable unbiased discovery of cell subsets. Many of these advanced computational methods and algorithmic frameworks are implemented and readily accessible through R-based packages.
While cytometry enthusiasts often express interests in learning R, significant barriers to learning exist. For those without access to institutional computational cytometry expertise, the question is often where to start. As the few existing online resources are primarily aimed at those with intermediate bioinformatic skills, self-study attempts often end in frustration due to lack of beginner suitable materials and troubleshooting support.
To address this community need, we developed free weekly ‘Cytometry in R’ course, designed for researchers with prior flow cytometry experience but no coding background. The course focuses on one topic each week, and is offered both in-person and via YouTube livestream, with recordings made available immediately after (https://www.youtube.com/@CytometryInR). All code and course materials are accessible via a GitHub repository(https://umgcccfcsr.github.io/CytometryInR/). In our commitment to open-science, all teaching materials are licensed under CC-BY-SA, and code is distributed under the copyleft AGPL3-0 license. A moderated discussion forum further supports participants by enabling troubleshooting, Q&A, and continued exploration of course topics.
Launched in February 2026, the course has generated substantial community interest, with over 1988 individuals completing the interest form. Using various metrics (Google Analytics, YouTube views, GitHub fork updates) around 500 participants have started the course, with between 250 and 350 active weekly participants. Overall reception has been highly positive, and the course continues to evolve in response to participant feedback and ongoing evaluation.
The course is comprehensive, with over 30 weeks of topics planned, intended to provide beginners solid foundations in R before moving on to intermediate and advanced cytometry topics as their coding skills and troubleshooting expertise develops. As all recordings and teaching materials will remain freely available after course completion, we hope to significantly reduce the existing barriers to analyzing cytometry data in R, permitting anyone who starts their own self-study journey in the future has a smoother journey than the one we ourselves experienced when first getting started.
License
In our commitment to open-science and open-source, all teaching materials are freely offered under a CC-BY-SA license, while all code examples are offered under the AGPL3-0 copyleft license.
