CytoVI: deep generative modeling of antibody-based single cell data

Antibody-based single-cell technologies, such as flow cytometry, mass cytometry and CITE-seq, have become widely used in clinical diagnostics and basic research; however, their analysis is complicated by technical noise, batch effects, platform differences and restricted antibody panels. Here we present CytoVI, a probabilistic generative model for statistically rigorous unified analysis of antibody-based single-cell data. CytoVI generates informative cell embeddings, imputes missing measurements, performs differential protein expression testing and automates annotation of cells in a single probabilistic model. We applied CytoVI to build an integrated B cell maturation atlas spanning 350 proteins and identified proteins associated with immunoglobulin class-switching. In a cohort of patients with B cell non-Hodgkin lymphoma profiled by flow cytometry and CITE-seq, CytoVI uncovered disease-associated T cell states. CytoVI is available as open-source software at scvi-tools.org . CytoVI is a deep generative model for statistically rigorous analysis of antibody-based single-cell data.

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Publication Details

Journal
Nature Methods
Published
2026-09-30
DOI
https://doi.org/10.1038/s41592-026-03224-5
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

CytoVI: deep generative modeling of antibody-based single cell data

Tobias Wertheimer, Jonas Maaskola, Nir Yosef, Nathan Levy et al.
Nature Methods
Single-cell and spatial transcriptomics
article

CytoVI: deep generative modeling of antibody-based single cell data

Tobias Wertheimer, Jonas Maaskola, Nir Yosef, Nathan Levy, Alexander Becker, Ido Amit, Corinne C. Widmer, Pierre Boyeau, Florian Ingelfinger, Robert Zeiser, Can Ergen, Martin Kim, Artemy Bakulin, Diana Ditz, Jan Dirks
article en

Abstract

Antibody-based single-cell technologies, such as flow cytometry, mass cytometry and CITE-seq, have become widely used in clinical diagnostics and basic research; however, their analysis is complicated by technical noise, batch effects, platform differences and restricted antibody panels. Here we present CytoVI, a probabilistic generative model for statistically rigorous unified analysis of antibody-based single-cell data. CytoVI generates informative cell embeddings, imputes missing measurements, performs differential protein expression testing and automates annotation of cells in a single probabilistic model. We applied CytoVI to build an integrated B cell maturation atlas spanning 350 proteins and identified proteins associated with immunoglobulin class-switching. In a cohort of patients with B cell non-Hodgkin lymphoma profiled by flow cytometry and CITE-seq, CytoVI uncovered disease-associated T cell states. CytoVI is available as open-source software at scvi-tools.org . CytoVI is a deep generative model for statistically rigorous analysis of antibody-based single-cell data.

Nature Methods
University of Freiburg (DE), German Cancer Research Center (DE), Heidelberg University (DE), University of Würzburg (DE), University Medical Center Freiburg (DE), University Hospital of Basel (CH), Weizmann Institute of Science (IL), University of California, Berkeley (US)
Openalex Percentile: Top 19%
Single-cell and spatial transcriptomics
DOIOpen Access PDFOpenAlex
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