Determining Clinically Relevant Features in Cytometry Data Using Persistent Homology

Author Summary: Identifying differences between cytometry data seen as a point cloud can be complicated by random variations in data collection and data sources. We apply used in to describe the shape and structure of the data representing immune cells in healthy donors and COVID-19 patients. By looking at how the shape and structure differ between healthy donors and COVID-19 patients, we are able to definitively conclude how these groups differ despite random variations in the data. Furthermore, these results are novel in their ability to capture shape and structure of cytometry data, something not described by other analyses.