Multiscale PHATE identifies multimodal signatures of COVID-19.
Nat Biotechnol
; 40(5): 681-691, 2022 05.
Article
in English
| MEDLINE | ID: covidwho-1713197
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 CD16hiCD66blo neutrophil and IFN-γ+ 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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Single-Cell Analysis
/
COVID-19
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
Nat Biotechnol
Journal subject:
Biotechnology
Year:
2022
Document Type:
Article
Affiliation country:
S41587-021-01186-x
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