Multiscale PHATE identifies multimodal signatures of COVID-19.

ABSTRACT: As the biomedical community produces datasets that are increasingly complex and high dimensional, there is a need for more sophisticated computational tools to extract biological insights. We present Multiscale PHATE, a method that sweeps through all levels of data granularity to learn abstracted biological features directly predictive of disease outcome. Built on a coarse-graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse resolutions for high-level summarizations of data and at fine resolutions for detailed representations of subsets. We apply Multiscale PHATE to a coronavirus disease 2019 (COVID-19) dataset with 54 million cells from 168 hospitalized patients and find that patients who die show CD16^hiCD66b

SUBMITTER: Kuchroo M 

PROVIDER: S-EPMC10015653 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature