ArticleBioinformatics (Oxford, England)2017
Automated cell type discovery and classification through knowledge transfer.
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 28 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
28 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
- T cell immunomonitoring: a comparative analysis of traditional and novel methods to quantify and characterize human antigen-specific T cells.Nature protocols · 2026Review
- Navigating the data processing for cytometry-based single-cell proteomics.Nature protocols · 2026Review
- Reducing Bias in Mass Cytometry Data through Corrections for Spillover and Nonspecific Binding.Analytical chemistry · 2026Article
- Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes.Bioinformatics advances · 2026Article
- 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
- Tribus: semi-automated discovery of cell identities and phenotypes from multiplexed imaging and proteomic data.Bioinformatics (Oxford, England) · 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
- ImmCellTyper facilitates systematic mass cytometry data analysis for deep immune profiling.eLife · 2024Article
- GateMeClass: Gate Mining and Classification of cytometry data.Bioinformatics (Oxford, England) · 2024Article
- The impacts of active and self-supervised learning on efficient annotation of single-cell expression data.Nature communications · 2024Article
- Cytomulate: accurate and efficient simulation of CyTOF data.Genome biology · 2023Article
- A review on deep learning applications in highly multiplexed tissue imaging data analysis.Frontiers in bioinformatics · 2023Review
- Different approaches to Imaging Mass Cytometry data analysis.Bioinformatics advances · 2023Review
- Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA.Nature methods · 2022Article
- CyTOFFrontiers in immunology · 2022Review
- CytoPy: An autonomous cytometry analysis framework.PLoS computational biology · 2021Article
- Progress and applications of mass cytometry in sketching immune landscapes.Clinical and translational medicine · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivationRecent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and hinders translation of new biological understanding into clinical applications. Previous studies have applied machine learning to facilitate processing of mass cytometry data. However, manual inspection is still inevitable and becoming the barrier to reliable large-scale analysis.
resultsWe present a new algorithm called utomated ell-type iscovery and lassification (ACDC) that fully automates the classification of canonical cell populations and highlights novel cell types in mass cytometry data. Evaluations on real-world data show ACDC provides accurate and reliable estimations compared to manual gating results. Additionally, ACDC automatically classifies previously ambiguous cell types to facilitate discovery. Our findings suggest that ACDC substantially improves both reliability and interpretability of results obtained from high-dimensional mass cytometry profiling data. AVAILABILITY AND IMPLEMENTATION: A Python package (Python 3) and analysis scripts for reproducing the results are availability on https://bitbucket.org/dudleylab/acdc . CONTACT: brian.kidd@mssm.edu or joel.dudley@mssm.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.