Multiscale PHATE identifies multimodal signatures of COVID-19

被引：44

作者：

Kuchroo, Manik ^{[1
]}

Huang, Jessie ^{[2
]}

Wong, Patrick ^{[3
]}

Grenier, Jean-Christophe ^{[4
]}

Shung, Dennis ^{[5
]}

Tong, Alexander ^{[2
]}

Lucas, Carolina ^{[3
]}

Klein, Jon ^{[3
]}

Burkhardt, Daniel B. ^{[6
]}

Gigante, Scott ^{[7
]}

Godavarthi, Abhinav ^{[8
]}

Rieck, Bastian ^{[9
]}

Israelow, Benjamin ^{[3
,10
]}

Simonov, Michael ^{[5
]}

Mao, Tianyang ^{[3
]}

Oh, Ji Eun ^{[3
]}

Silva, Julio ^{[3
]}

Takahashi, Takehiro ^{[3
]}

Odio, Camila D. ^{[5
]}

Casanovas-Massana, Arnau ^{[11
]}

Farhadian, Shelli ^{[10
]}

Dela Cruz, Charles S. ^{[12
,13
]}

Ko, Albert I. ^{[10
,11
]}

Hirn, Matthew J. ^{[14
,15
]}

Wilson, F. Perry ^{[16
]}

Hussin, Julie G. ^{[4
,17
]}

Wolf, Guy ^{[18
,19
]}

Iwasaki, Akiko ^{[3
,20
]}

Krishnaswamy, Smita ^{[2
,6
]}

机构：

[1] Yale Univ, Dept Neurosci, New Haven, CT USA

[2] Yale Univ, Dept Comp Sci, POB 2158, New Haven, CT 06520 USA

[3] Yale Univ, Dept Immunobiol, New Haven, CT USA

[4] Montreal Heart Inst, Montreal, PQ, Canada

[5] Yale Univ, Dept Med, New Haven, CT 06520 USA

[6] Yale Univ, Dept Genet, New Haven, CT 06520 USA

[7] Yale Univ, Bioinformat Program, Computat Biol, New Haven, CT USA

[8] Yale Univ, Dept Appl Math, New Haven, CT USA

[9] Swiss Fed Inst Technol, Dept Biosyst Sci & Engn, Zurich, Switzerland

[10] Yale Univ, Sch Med, Dept Med, Sect Infect Dis, New Haven, CT 06510 USA

[11] Yale Sch Publ Hlth, Dept Epidemiol Microbial Dis, New Haven, CT USA

[12] Yale Univ, Sch Med, Dept Med, Sect Pulm & Crit Care Med, New Haven, CT 06510 USA

[13] West Haven Connecticut Vet Affairs Med Ctr, Dept Med, West Haven, CT USA

[14] Michigan State Univ, Dept Math, E Lansing, MI 48824 USA

[15] Michigan State Univ, Dept Computat Math Sci & Engn, E Lansing, MI 48824 USA

[16] Yale Univ, Dept Med, Clin & Translat Res Accelerator, New Haven, CT 06520 USA

[17] Univ Montreal, Fac Med, Montreal, PQ, Canada

[18] Mila Quebec AI Inst, Montreal, PQ, Canada

[19] Univ Montreal, Dept Math & Stat, Montreal, PQ, Canada

[20] Howard Hughes Med Inst, Chevy Chase, MD USA

来源：

NATURE BIOTECHNOLOGY | 2022年 / 40卷 / 05期

基金：

美国国家科学基金会; 加拿大自然科学与工程研究理事会;

关键词：

T-CELL; NEUTROPHILS; EXPRESSION;

D O I：

10.1038/s41587-021-01186-x

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

Disease signatures in high-dimensional biomedical data are detected with a visualization algorithm. 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(hi)CD66b(lo) 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.

引用

页码：681 / +

页数：27

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