Integrated analysis of multimodal single-cell data.
Cell
; 184(13): 3573-3587.e29, 2021 06 24.
Article
in English
| MEDLINE | ID: covidwho-1248834
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce "weighted-nearest neighbor" analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Single-Cell Analysis
/
SARS-CoV-2
Type of study:
Prognostic study
Topics:
Vaccines
Limits:
Animals
/
Humans
Language:
English
Journal:
Cell
Year:
2021
Document Type:
Article
Affiliation country:
J.cell.2021.04.048
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