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Multiscale PHATE identifies multimodal signatures of COVID-19.
Kuchroo, Manik; Huang, Jessie; Wong, Patrick; Grenier, Jean-Christophe; Shung, Dennis; Tong, Alexander; Lucas, Carolina; Klein, Jon; Burkhardt, Daniel B; Gigante, Scott; Godavarthi, Abhinav; Rieck, Bastian; Israelow, Benjamin; Simonov, Michael; Mao, Tianyang; Oh, Ji Eun; Silva, Julio; Takahashi, Takehiro; Odio, Camila D; Casanovas-Massana, Arnau; Fournier, John; Farhadian, Shelli; Dela Cruz, Charles S; Ko, Albert I; Hirn, Matthew J; Wilson, F Perry; Hussin, Julie G; Wolf, Guy; Iwasaki, Akiko; Krishnaswamy, Smita.
  • Kuchroo M; Department of Neuroscience, Yale University, New Haven, CT, USA.
  • Huang J; Department of Computer Science, Yale University, New Haven, CT, USA.
  • Wong P; Department of Immunobiology, Yale University, New Haven, CT, USA.
  • Grenier JC; Montreal Heart Institute, Montreal, Quebec, Canada.
  • Shung D; Department of Medicine, Yale University, New Haven, CT, USA.
  • Tong A; Department of Computer Science, Yale University, New Haven, CT, USA.
  • Lucas C; Department of Immunobiology, Yale University, New Haven, CT, USA.
  • Klein J; Department of Immunobiology, Yale University, New Haven, CT, USA.
  • Burkhardt DB; Department of Genetics, Yale University, New Haven, CT, USA.
  • Gigante S; Computational Biology, Bioinformatics Program, Yale University, New Haven, CT, USA.
  • Godavarthi A; Department of Applied Mathematics, Yale University, New Haven, CT, USA.
  • Rieck B; Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland.
  • Israelow B; Department of Immunobiology, Yale University, New Haven, CT, USA.
  • Simonov M; Section of Infectious Diseases, Department of Medicine, Yale University School of Medicine, New Haven, CT, USA.
  • Mao T; Department of Medicine, Yale University, New Haven, CT, USA.
  • Oh JE; Department of Immunobiology, Yale University, New Haven, CT, USA.
  • Silva J; Department of Immunobiology, Yale University, New Haven, CT, USA.
  • Takahashi T; Department of Immunobiology, Yale University, New Haven, CT, USA.
  • Odio CD; Department of Immunobiology, Yale University, New Haven, CT, USA.
  • Casanovas-Massana A; Department of Medicine, Yale University, New Haven, CT, USA.
  • Fournier J; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
  • Dela Cruz CS; Section of Infectious Diseases, Department of Medicine, Yale University School of Medicine, New Haven, CT, USA.
  • Ko AI; Section of Pulmonary and Critical Care Medicine, Department of Medicine, Yale University School of Medicine, New Haven, CT, USA.
  • Hirn MJ; Department of Medicine, West Haven Connecticut Veterans Affairs Medical Center, West Haven, CT, USA.
  • Wilson FP; Section of Infectious Diseases, Department of Medicine, Yale University School of Medicine, New Haven, CT, USA.
  • Hussin JG; Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA.
  • Wolf G; Department of Mathematics, Michigan State University, East Lansing, MI, USA.
  • Iwasaki A; Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA.
  • Krishnaswamy S; Clinical and Translational Research Accelerator, Department of Medicine, Yale University, New Haven, CT, USA.
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.
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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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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