ArticleiScience2022
Dimensionality reduction by UMAP for visualizing and aiding in classification of imaging flow cytometry data.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- ANLN: A New Hub in Glutamine Metabolism of Lung Adenocarcinoma by scRNA-Seq and Machine Learning.Oncology research · 2026Article
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- Automatic computational classification of bone marrow cells for B cell pediatric leukemia using UMAP.BioData mining · 2025Article
- Alzheimer's subtypes A supervised, unsupervised, multimodal, multilayered embedded recursive (SUMMER) AI study.bioRxiv : the preprint server for biology · 2025Article
- SingletSeeker: An unsupervised clustering approach for automated singlet discrimination in cytometry.Cytometry. Part B, Clinical cytometry · 2025Article
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- Nuclear morphometrics coupled with machine learning identifies dynamic states of senescence across age.Nature communications · 2025Article
- Tracking Oral Nanoparticle Uptake in Mouse Gastrointestinal Tract by Fluorescent Labeling and t-SNE Flow Cytometry.Bio-protocol · 2025Article
- Framework for investigating structure cracking using real engineering data combined with physics constraints.Scientific reports · 2025Article
- Elevated frequencies of activated memory B cells in multiple sclerosis are reset to healthy control levels after B cell depletion with Ocrelizumab.Journal of neuroimmunology · 2025Article
- Haematology dimension reduction, a large scale application to regular care haematology data.BMC medical informatics and decision making · 2025Article
- Stem cell factor and erythropoietin-independent production of cultured reticulocytes.Haematologica · 2024Article
- The shared role of neutrophils in ankylosing spondylitis and ulcerative colitis.Genes and immunity · 2024Article
- Addressing label noise for electronic health records: insights from computer vision for tabular data.BMC medical informatics and decision making · 2024Article
- Systems biology of B cells in COVID-19.Seminars in immunology · 2024Review
- Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.The New phytologist · 2023Article
- Spatial modelling of the tumor microenvironment from multiplex immunofluorescence images: methods and applications.Frontiers in immunology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in imaging flow cytometry (IFC) have revolutionized high-throughput multiparameter analyses at single-cell resolution. Although enabling the discovery of population heterogeneities and the detection of rare events, IFC generates hyperdimensional datasets that demand innovative analytical approaches. Current methods work in a supervised manner, utilize only limited information content, or require large annotated reference datasets. Dimensionality reduction algorithms, including uniform manifold approximation and projection (UMAP), have been successfully applied to analyze the large number of parameters generated in various high-throughput techniques. Here, we apply a workflow incorporating UMAP to analyze different IFC datasets. We demonstrate that it out-competes other popular dimensionality reduction methods in speed and accuracy. Moreover, it enables fast visualization, clustering, and tagging of unannotated objects in large-scale experiments. We anticipate that our workflow will be a robust method to address complex IFC datasets, either alone or as an upstream addition to the deep learning approaches.
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Registered trials
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