19抖阴

Creating and evaluating a repository of synthetic flow cytometry files for educational use

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Mastering data analysis and interpretation is crucial for Biomedical Scientists. In this interdisciplinary project, students will develop methods to create synthetic flow cytometry datasets. These innovative methods will be used to build a comprehensive data library complete with engaging case studies. These tools will be rigorously evaluated as transformative educational resources.

Award
MRes | MPhil | PhD | MSc by Research
Start Date
Usually February and October - at individual School's discretion

Summary

Flow cytometry is a powerful technique used in both diagnostic and research laboratories to evaluate characteristics of mixed cell populations. A high level of skill is required to properly analyse and interpret flow cytometry data, without high-quality training materials, the risk of misinterpretation is high.

A key limitation to the provision of high-quality training is the lack of high-quality flow cytometry data available in the public domain. This is especially true when trying to match available data with case studies relevant to Biomedical Scientists working in diagnostic labs. Generating bespoke data sets can be costly and time-consuming and will not be possible for smaller teaching universities.

This project that aims to create a repository of synthetic flow cytometry data files for educational use. In flow cytometry, cells suspended in a fluid are passed through a laser beam one at a time. Each cell interacts with the laser, and various detectors measure parameters like forward scatter (cell size), side scatter (cell complexity) and fluorescent signals emitted by a panel of fluorescent labels used to mark various features of the cell. For a single sample (e.g. a patient blood sample), 30,000 individual cells might be analysed, with 20 different parameters measured for each cell. Multiple different cell types will be represented in each sample / data file, and the goal of data analysis is to identify different cell types and describe their characteristics. This is achieved by hierarchical gating on populations of interest, or using dimensionality reduction (UMAP, t-SNE), clustering (K-Means) or classification via machine learning.;

The student will use computational methods to generate a repository of synthetic data files. The student will also develop engaging case studies and training materials, and these novel educational tools will be tested in real classroom settings, improving the standard of training for future Biomedical and research scientists.

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