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Multiscale PHATE identifies multimodal signatures of COVID-19.


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 CD16hiCD66blo neutrophil and IFN-gamma+ granzyme B+ Th17 cell responses. We also show that population groupings from Multiscale PHATE directly fed into a classifier predict disease outcome more accurately than naive featurizations of the data. Multiscale PHATE is broadly generalizable to different data types, including flow cytometry, single-cell RNA sequencing (scRNA-seq), single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq), and clinical variables.

Authors: Kuchroo M, Huang J, Wong P, Grenier JC, Shung D, Tong A, Lucas C, Klein J, Burkhardt DB, Gigante S, Godavarthi A, Rieck B, Israelow B, Simonov M, Mao T, Oh JE, Silva J, Takahashi T, Odio CD, Casanovas-Massana A, Fournier J, Yale IMPACT Team, Farhadian S,
Journal: Nat Biotechnol;2022 May;40(5):681-691. doi:10.1038/s41587-021-01186-x
Year: 2022
PubMed: PMID: 35228707 (Go to PubMed)