ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2016
A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed.
- Self-explaining artificial intelligence for the classification of B cell non-Hodgkin lymphoma: A diagnostic decision support study.PLoS medicine · 2026Article
- SingletSeeker: An unsupervised clustering approach for automated singlet discrimination in cytometry.Cytometry. Part B, Clinical cytometry · 2025Article
- Machine Learning Methods in Clinical Flow Cytometry.Cancers · 2025Review
- Lifting the curse from high-dimensional data: automated projection pursuit clustering for a variety of biological data modalities.GigaScience · 2025Article
- CytoPipeline and CytoPipelineGUI: a Bioconductor R package suite for building and visualizing automated pre-processing pipelines for flow cytometry data.BMC bioinformatics · 2024Article
- PeacoQC: Peak-based selection of high quality cytometry data.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2022Article
- New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy.Patterns (New York, N.Y.) · 2021Article
- CytoPy: An autonomous cytometry analysis framework.PLoS computational biology · 2021Article
- Censcyt: censored covariates in differential abundance analysis in cytometry.BMC bioinformatics · 2021Article
- Computational Analysis of Microbial Flow Cytometry Data.mSystems · 2021Review
- Implementing flowDensity for Automated Analysis of Bone Marrow Lymphocyte Population.Journal of pathology informatics · 2021Article
- Single-cell peripheral immunoprofiling of Alzheimer's and Parkinson's diseases.Science advances · 2020Article
- Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities.Nature reviews. Neurology · 2020Review
- Improving the Rigor and Reproducibility of Flow Cytometry-Based Clinical Research and Trials Through Automated Data Analysis.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2020Article
- Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry.Computational and structural biotechnology journal · 2020Review
- Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition).European journal of immunology · 2019Article
- Improving the Quality and Reproducibility of Flow Cytometry in the Lung. An Official American Thoracic Society Workshop Report.American journal of respiratory cell and molecular biology · 2019Article
- Essential guidelines for computational method benchmarking.Genome biology · 2019Review
- An integrated framework using high-dimensional mass cytometry and fluorescent flow cytometry identifies discrete B cell subsets in patients with red meat allergy.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2019Article
- How Clinical Flow Cytometry Rebooted Sepsis Immunology.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2019Review
Corrections and comments
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Authors and funding
23 authors.
Funding
Abstract
The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. Here we report the results of FlowCAP-IV where algorithms from seven different research groups predicted the time to progression to AIDS among a cohort of 384 HIV+ subjects, using antigen-stimulated peripheral blood mononuclear cell (PBMC) samples analyzed with a 14-color staining panel. Two approaches (FlowReMi.1 and flowDensity-flowType-RchyOptimyx) provided statistically significant predictive value in the blinded test set. Manual validation of submitted results indicated that unbiased analysis of single cell phenotypes could reveal unexpected cell types that correlated with outcomes of interest in high dimensional flow cytometry datasets.
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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.