ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2014
SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 2: biological evaluation.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed.
- Rapid reduction in Staphylococcus aureus in atopic dermatitis subjects following dupilumab treatment.The Journal of allergy and clinical immunology · 2023Trial
- Trial
- AutoFlow: an interactive Shiny app for supervised and unsupervised flow cytometry analysis.Bioinformatics (Oxford, England) · 2026Article
- Farm exposure in infancy is associated with elevated systemic IgGScience translational medicine · 2025Article
- A Phenotypically Distinct Human Th2 Cell Subpopulation Is Associated With Development of Allergic Disorders in Infancy.Allergy · 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
- Measurable (Minimal) Residual Disease in Myelodysplastic Neoplasms (MDS): Current State and Perspectives.Cancers · 2024Review
- SWIFT clustering analysis of intracellular cytokine staining flow cytometry data of the HVTN 105 vaccine trial reveals high frequencies of HIV-specific CD4+ T cell responses and associations with humoral responses.Frontiers in immunology · 2024Article
- Comparative analysis of dimension reduction methods for cytometry by time-of-flight data.Nature communications · 2023Article
- Building predictive disease models using extracellular vesicle microscale flow cytometry and machine learning.Molecular oncology · 2023Article
- Fast quantification of gut bacterial species in cocultures using flow cytometry and supervised classification.ISME communications · 2022Article
- Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing.International journal of molecular sciences · 2022Article
- Monitoring minimal/measurable residual disease in B-cell acute lymphoblastic leukemia by flow cytometry during targeted therapy.International journal of hematology · 2021Review
- Immunity to Influenza Infection in Humans.Cold Spring Harbor perspectives in medicine · 2021Review
- High Throughput Multi-Omics Approaches for Clinical Trial Evaluation and Drug Discovery.Frontiers in immunology · 2021Review
- Application of Machine Learning for Cytometry Data.Frontiers in immunology · 2021Review
- PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells.Bioinformatics (Oxford, England) · 2020Article
- Review
- Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data.Frontiers in cell and developmental biology · 2020Review
- Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition).European journal of immunology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
A multistage clustering and data processing method, SWIFT (detailed in a companion manuscript), has been developed to detect rare subpopulations in large, high-dimensional flow cytometry datasets. An iterative sampling procedure initially fits the data to multidimensional Gaussian distributions, then splitting and merging stages use a criterion of unimodality to optimize the detection of rare subpopulations, to converge on a consistent cluster number, and to describe non-Gaussian distributions. Probabilistic assignment of cells to clusters, visualization, and manipulation of clusters by their cluster medians, facilitate application of expert knowledge using standard flow cytometry programs. The dual problems of rigorously comparing similar complex samples, and enumerating absent or very rare cell subpopulations in negative controls, were solved by assigning cells in multiple samples to a cluster template derived from a single or combined sample. Comparison of antigen-stimulated and control human peripheral blood cell samples demonstrated that SWIFT could identify biologically significant subpopulations, such as rare cytokine-producing influenza-specific T cells. A sensitivity of better than one part per million was attained in very large samples. Results were highly consistent on biological replicates, yet the analysis was sensitive enough to show that multiple samples from the same subject were more similar than samples from different subjects. A companion manuscript (Part 1) details the algorithmic development of SWIFT.
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.