ArticleFrontiers in immunology2021
Unsupervised Analysis of Flow Cytometry Data in a Clinical Setting Captures Cell Diversity and Allows Population Discovery.
Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Unsupervised Flow Cytometry Reveals a Constant Shift Towards Activated CD4Scandinavian journal of immunology · 2026Article
- Integrating Machine Learning and Multi-Omics to Explore Neutrophil Heterogeneity.Biomedicines · 2025Review
- Elucidating Immune Cell Changes in Celiac Disease: Revealing New Insights from Spectral Flow Cytometry.International journal of molecular sciences · 2025Article
- AI-assisted peripheral immune profiling reveals unconventional lymphocyte signatures associated with prognosis in soft tissue sarcoma patients.Frontiers in immunology · 2025Article
- Circulating CD8 T cells from patients with mild-to-moderate psoriasis are functionally impaired.Frontiers in immunology · 2025Article
- Applications of Flow Cytometry in Drug Discovery and Translational Research.International journal of molecular sciences · 2024Review
- Investigating Vα7.2International journal of molecular sciences · 2024Article
- flowSim: Near duplicate detection for flow cytometry data.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2023Article
- Improving Multicolor Colocalization in Single-Vesicle Flow Cytometry with Vesicle Transit Time.Analytical chemistry · 2023Article
- Time series clustering of T cell subsets dissects heterogeneity in immune reconstitution and clinical outcomes among MUD-HCT patients receiving ATG or PTCy.Frontiers in immunology · 2023Article
- The impact of single-cell genomics on the field of mycobacterial infection.Frontiers in microbiology · 2022Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data obtained with cytometry are increasingly complex and their interrogation impacts the type and quality of knowledge gained. Conventional supervised analyses are limited to pre-defined cell populations and do not exploit the full potential of data. Here, in the context of a clinical trial of cancer patients treated with radiotherapy, we performed longitudinal flow cytometry analyses to identify multiple distinct cell populations in circulating whole blood. We cross-compared the results from state-of-the-art recommended supervised analyses with results from MegaClust, a high-performance data-driven clustering algorithm allowing fast and robust identification of cell-type populations. Ten distinct cell populations were accurately identified by supervised analyses, including main T, B, dendritic cell (DC), natural killer (NK) and monocytes subsets. While all ten subsets were also identified with MegaClust, additional cell populations were revealed (e.g. CD4
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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.