ArticleBioinformatics (Oxford, England)2017
Gating mass cytometry data by deep learning.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated and reproducible cell identification in mass cytometry using neural networks.Briefings in bioinformatics · 2023Pooled it
- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- dioscRi enables transferable prediction of clinical outcomes in multi-parameter cytometry data.Nature communications · 2026Article
- Navigating the Landscape of Cytometry-Based Single-Cell Proteomics: Quantification, Annotation, and Resources.International journal of molecular sciences · 2026Review
- Recent advances in machine learning-enhanced extracellular vesicle omics for oncology.Journal of nanobiotechnology · 2026Review
- Application and characterization of the multiple instance learning framework in flow cytometry.Scientific reports · 2026Article
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- Automated descriptive cell type naming in flow and mass cytometry with CytoPheno.Scientific reports · 2025Article
- Integrated workflow for analysis of immune enriched spatial proteomic data with IMmuneCite.Scientific reports · 2025Article
- Automated cytometric gating with human-level performance using bivariate segmentation.Nature communications · 2025Article
- Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence.Journal of biomedical science · 2025Review
- Optimal transport reveals immune perturbation and fingerprints over time in COVID-19 vaccination.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating.Briefings in bioinformatics · 2024Article
- Cytometry masked autoencoder: An accurate and interpretable automated immunophenotyper.Cell reports. Medicine · 2024Article
- RUCova: Removal of Unwanted Covariance in mass cytometry data.Bioinformatics (Oxford, England) · 2024Article
- ImmCellTyper facilitates systematic mass cytometry data analysis for deep immune profiling.eLife · 2024Article
- MetaGate: Interactive analysis of high-dimensional cytometry data with metadata integration.Patterns (New York, N.Y.) · 2024Article
- Recent Developments in Machine Learning for Mass Spectrometry.ACS measurement science au · 2024Review
- Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation.bioRxiv : the preprint server for biology · 2024Article
- GateMeClass: Gate Mining and Classification of cytometry data.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
motivationMass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve automation, scalability, performance and interpretation of data generated in large studies. Assigning individual cells into discrete groups of cell types (gating) involves time-consuming sequential manual steps, untenable for larger studies.
resultsWe introduce DeepCyTOF, a standardization approach for gating, based on deep learning techniques. DeepCyTOF requires labeled cells from only a single sample. It is based on domain adaptation principles and is a generalization of previous work that allows us to calibrate between a target distribution and a source distribution in an unsupervised manner. We show that DeepCyTOF is highly concordant (98%) with cell classification obtained by individual manual gating of each sample when applied to a collection of 16 biological replicates of primary immune blood cells, even when measured across several instruments. Further, DeepCyTOF achieves very high accuracy on the semi-automated gating challenge of the FlowCAP-I competition as well as two CyTOF datasets generated from primary immune blood cells: (i) 14 subjects with a history of infection with West Nile virus (WNV), (ii) 34 healthy subjects of different ages. We conclude that deep learning in general, and DeepCyTOF specifically, offers a powerful computational approach for semi-automated gating of CyTOF and flow cytometry data. AVAILABILITY AND IMPLEMENTATION: Our codes and data are publicly available at https://github.com/KlugerLab/deepcytof.git. CONTACT: yuval.kluger@yale.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.