School of Pharmacy, Applied Sciences and Public Health

Next‑Gen Diagnostics: Creating an Open Resource for Flow Cytometry Training

girl on a mountain looking at the sun rise

Flow cytometry is an important tool in both research and clinical laboratory diagnostics. High quality training materials are required to ensure accurate analysis and interpretation of the data, but good model data is difficult to access.

Award

MRes | MPhil | PhD | MSc by Research

Start Date

Usually February and October - at individual School's discretion

Research Opportunities

Summary

This project gives you hands‑on experience generating realistic synthetic datasets, refining an R‑based tool, and building an open educational resource used to train future biomedical scientists.

Flow cytometry is a commonly used technique in the diagnosis and monitoring of human disease and drug responses. It involves labelling mixed populations of cells with fluorescent markers, in order to better understand their composition and functional capabilities. A high level of skill is required to properly analyse and interpret flow cytometry data, and without high-quality training materials, the risk of misinterpretation is high. The provision of effective training materials is however limited by the availability of relevant, high-quality flow cytometry data in the public domain, and by the cost and ethical considerations associated with generating bespoke datasets from human samples.

This project tackles these problems directly by creating an open educational resource (OER) built around fully synthetic, biologically realistic flow cytometry datasets. These datasets can be shared freely, adapted to specific learning scenarios, and used without the constraints associated with human samples.

Students will:

Take ownership of a custom R-based synthetic data generator. The tool generates synthetic flow cytometry data files for user-defined biological scenarios. Students will improve, extend and validate this tool.
Work with experts in academia and the NHS to identify what features and learning scenarios are most valuable for teaching flow cytometry data analysis.
Generate a library of synthetic datasets mimicking diagnostically relevant profiles, paired with case studies and analysis workflows.
Evaluate how well these materials support student learning.

By the end of the project, you will have created a resource that supports the training of future biomedical and clinical scientists. You will also gain hands‑on experience in R programming, synthetic data generation, education research, user‑centred design and stakeholder engagement. These skills that are valuable across diagnostics, research and data‑driven healthcare.