ArticleBioinformatics (Oxford, England)2018
flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- CARGO: A Cytometry Analysis framework via Regularized Graph Optimal-transport.PLoS computational biology · 2026Article
- Navigating the Landscape of Cytometry-Based Single-Cell Proteomics: Quantification, Annotation, and Resources.International journal of molecular sciences · 2026Review
- Automated cytometric gating with human-level performance using bivariate segmentation.Nature communications · 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
- MetaGate: Interactive analysis of high-dimensional cytometry data with metadata integration.Patterns (New York, N.Y.) · 2024Article
- Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.Cytometry. Part B, Clinical cytometry · 2024Article
- Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation.bioRxiv : the preprint server for biology · 2024Article
- A cell-level discriminative neural network model for diagnosis of blood cancers.Bioinformatics (Oxford, England) · 2023Article
- Advances in automated real-time flow cytometry for monitoring of bioreactor processes.Engineering in life sciences · 2022Review
- 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
- optimalFlow: optimal transport approach to flow cytometry gating and population matching.BMC bioinformatics · 2020Article
- Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions.Nature machine intelligence · 2020Article
- A Cancer Biologist's Primer on Machine Learning Applications in High-Dimensional Cytometry.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2020Review
- Machine Learning of Discriminative Gate Locations for Clinical Diagnosis.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2020Article
- 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
- 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
- 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
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Motivation: Identification of cell populations in flow cytometry is a critical part of the analysis and lays the groundwork for many applications and research discovery. The current paradigm of manual analysis is time consuming and subjective. A common goal of users is to replace manual analysis with automated methods that replicate their results. Supervised tools provide the best performance in such a use case, however they require fine parameterization to obtain the best results. Hence, there is a strong need for methods that are fast to setup, accurate and interpretable. Results: flowLearn is a semi-supervised approach for the quality-checked identification of cell populations. Using a very small number of manually gated samples, through density alignments it is able to predict gates on other samples with high accuracy and speed. On two state-of-the-art datasets, our tool achieves median(F1)-measures exceeding 0.99 for 31%, and 0.90 for 80% of all analyzed populations. Furthermore, users can directly interpret and adjust automated gates on new sample files to iteratively improve the initial training. Availability and implementation: FlowLearn is available as an R package on https://github.com/mlux86/flowLearn. Evaluation data is publicly available online. Details can be found in the Supplementary Material. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.