ArticleCytometry. Part B, Clinical cytometry2018
PhenoGraph and viSNE facilitate the identification of abnormal T-cell populations in routine clinical flow cytometric data.
Article in Cytometry. Part B, Clinical cytometry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
18 citing papers in PubMed.
- CyTOF-Enabled Analysis Identifies Class-Switched B Cells as the Main Lymphocyte Subset Associated With Disease Relapse in Children With Idiopathic Nephrotic Syndrome.Frontiers in immunology · 2021Trial
- Canonical Notch2 signaling regulates development and maintenance of iron-recycling macrophages and iron homeostasis.Nature communications · 2026Article
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- Antibody-Based Multiplex Image Analysis: Standard Analytical Workflows and Artificial Intelligence Tools for Pathologists.Laboratory investigation; a journal of technical methods and pathology · 2025Review
- Clustering Algorithm-Driven Detection of TRBC1-Restricted Clonal T-Cell Populations Produces Better Results than Manual Gating Analysis.International journal of molecular sciences · 2024Article
- Phenotypical differences of neutrophils patrolling tumour-draining lymph nodes in head and neck cancer.British journal of cancer · 2024Article
- CelltypeR: A flow cytometry pipeline to characterize single cells from brain organoids.iScience · 2024Article
- Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders.Diagnostics (Basel, Switzerland) · 2024Article
- Single-cell mass cytometry in immunological skin diseases.Frontiers in immunology · 2024Review
- Role of circulating T follicular helper subsets following Ty21a immunization and oral challenge with wild typeFrontiers in immunology · 2024Article
- Diagnosis of Acute Leukemia by Multiparameter Flow Cytometry with the Assistance of Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2022Article
- Deep phenotyping of T cell populations under long-term treatment of tacrolimus and rapamycin in patients receiving renal transplantations by mass cytometry.Clinical and translational medicine · 2021Article
- Article
- Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition).European journal of immunology · 2019Article
- Differential Variation Analysis Enables Detection of Tumor Heterogeneity Using Single-Cell RNA-Sequencing Data.Cancer research · 2019Article
- Circulating B Cells With Memory and Antibody-Secreting Phenotypes Are Detectable in Pediatric Kidney Transplant Recipients Before the Development of Antibody-Mediated Rejection.Transplantation direct · 2019Article
- T-cell exhaustion correlates with improved outcomes in kidney transplant recipients.Kidney international · 2019Observational
- Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM).Current protocols in cytometry · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundFlow cytometric identification of neoplastic T-cell populations is complicated by the wide range of phenotypic abnormalities in T-cell neoplasia, and the diverse repertoire of reactive T-cell phenotypes. We evaluated whether a recently described clustering algorithm, PhenoGraph, and dimensionality-reduction algorithm, viSNE, might facilitate the identification of abnormal T-cell populations in routine clinical flow cytometric data.
methodsWe applied PhenoGraph and viSNE to peripheral blood mononuclear cells labeled with a single 8-color T/NK-cell antibody combination. Individual peripheral blood samples containing either a T-cell neoplasm or reactive lymphocytosis were analyzed together with a cohort of 10 normal samples, which established the location and identity of normal mononuclear-cell subsets in viSNE displays.
resultsPhenoGraph-derived subpopulations from the normal samples formed regions of phenotypic similarity in the viSNE display describing normal mononuclear-cell subsets, which correlated with those obtained by manual gating (r
conclusionsPhenoGraph and viSNE may facilitate the identification of abnormal T-cell populations in routine clinical flow cytometric data. © 2017 Clinical Cytometry Society.
Indexed as
Identifiers
What OpenQuestion holds
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.