Topological data analysis of collective and individual epithelial cells using persistent homology of loops.

ABSTRACT: Interacting, self-propelled particles such as epithelial cells can dynamically self-organize into complex multicellular patterns, which are challenging to classify without a priori information. Classically, different phases and phase transitions have been described based on local ordering, which may not capture structural features at larger length scales. Instead, topological data analysis (TDA) determines the stability of spatial connectivity at varying length scales (i.e. persistent homology), and can compare different particle configurations based on the "cost" of reorganizing one configuration into another. Here, we demonstrate a topology-based machine learning approach for unsupervised profiling of individual and collective phases based on large-scale loops. We show that these topolog

SUBMITTER: Bhaskar D 

PROVIDER: S-EPMC8276269 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature