Determining clinically relevant features in cytometry data using persistent homology.

ABSTRACT: Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. We present that persistent homology, a mathematical structure that summarizes the topological features, can distinguish different sources of data, such as from groups of healthy donors or patients, effectively. Analysis of publicly available cytometry data describing non-naïve CD8+ T cells in COVID-19 patients an

SUBMITTER: Mukherjee S 

PROVIDER: S-EPMC9009779 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature