MUDAFACS - Towards a multivariate data analysis of categorical and flow cytometry (FACS) data: use case analysis of samples from COVID-19 patients
Project duration: 2021-2022
- Dr. Richard Ottermanns (firstname.lastname@example.org)
- Dr. Ana Izcue (email@example.com)
- MSc Katja Schröder (firstname.lastname@example.org)
Description of the Project:
Recent technological developments in flow cytometry have considerably increased the number of parameters that can be analyzed per cell. These advances are not matched yet by the analytical tools, and there is currently a dire need for statistical methods allowing to identify which parameters drive differences between samples. New tools are also urgently needed, especially in clinical research, to handle a high number of numerical parameters and combine them with non-cytometrical information, such as age, sex, and severity of disease. Standard approaches from ecotoxicology and ecological data analysis can potentially be transferred to flow cytometry data analysis to achieve this. In this project novel approaches to analyze the wealth of data that derives from high-end flow cytometry will be defined and combined with non-numerical, categorical information. As a use case, flow cytometry to characterize T lymphocytes in COVID-19 patients will be used and the information combined with clinical metadata. T lymphocytes are key for the defense against pathogens, and dysfunctional T cell responses have been linked to the damage occurring in patients with severe COVID-19 disease. The phenotype of T cells in COVID-19 samples is very heterogeneous. Statistical methods to tackle this heterogeneity will be developed. Based on dimensionality reduction approaches a new constrained algorithm will be developed to depict the contribution of variables using a regression based technique, and the results will be correlated to mixed numerical and categorical metadata like sex and age. In this way this project will set up a basis to identify the main flow-cytometry variables driving differences among samples and quantify the impact of categorical variables on cytometric patterns. The tools will be made applicable to any data set combining flow cytometric and other data, which will be instrumental for the biomedical and ecotoxicology communities.
MUDAFACS is an interdisciplinary cooperation between the Institute for Environmental Research and the Institute for Molecular Medicine (Uniklinik Aachen), funded by the RWTH Aachen Exploratory Research Space (ERS).