ArticleBMC bioinformatics2016
BayesFlow: latent modeling of flow cytometry cell populations.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers.
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17 citing papers in PubMed.
- A multilevel hierarchical framework for quantification of experimental heterogeneity in population snapshot data.PLoS computational biology · 2026Article
- Measurable residual disease testing in acute myeloid leukemia: current state, foundational models, and tools for future development.Cancer metastasis reviews · 2026Review
- Computationally efficient multi-sample flow cytometry data analysis using Gaussian mixture models.BMC bioinformatics · 2025Article
- A variational deep-learning approach to modeling memory T cell dynamics.PLoS computational biology · 2025Article
- A variational deep-learning approach to modeling memory T cell dynamics.bioRxiv : the preprint server for biology · 2025Article
- AutoGater: a weakly supervised neural network model to gate cells in flow cytometric analyses.Scientific reports · 2024Article
- Visualizing scRNA-Seq data at population scale with GloScope.Genome biology · 2024Article
- Visualizing scRNA-Seq Data at Population Scale with GloScope.bioRxiv : the preprint server for biology · 2024Article
- Restricting datasets to classifiable samples augments discovery of immune disease biomarkers.Nature communications · 2024Article
- A Bayesian feature allocation model for identifying cell subpopulations using CyTOF data.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- Identifying cell-to-cell variability in internalization using flow cytometry.Journal of the Royal Society, Interface · 2022Article
- UMAP Based Anomaly Detection for Minimal Residual Disease Quantification within Acute Myeloid Leukemia.Cancers · 2022Article
- Machine Learning of Discriminative Gate Locations for Clinical Diagnosis.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2020Article
- Ultrafast clustering of single-cell flow cytometry data using FlowGrid.BMC systems biology · 2019Article
- MEMO: multi-experiment mixture model analysis of censored data.Bioinformatics (Oxford, England) · 2016Article
- Computational flow cytometry: helping to make sense of high-dimensional immunology data.Nature reviews. Immunology · 2016Review
- Erratum to: BayesFlow: latent modeling of flow cytometry cell populations.BMC bioinformatics · 2016Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFlow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, most automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. A key requirement for models that include multiple samples is the ability to visualize and assess inferred variation, since what could be technical variation in one setting would be different phenotypes in another.
resultsWe introduce BayesFlow, a pipeline for latent modeling of flow cytometry cell populations built upon a Bayesian hierarchical model. The model systematizes variation in location as well as shape. Expert knowledge can be incorporated through informative priors and the results can be supervised through compact and comprehensive visualizations. BayesFlow is applied to two synthetic and two real flow cytometry data sets. For the first real data set, taken from the FlowCAP I challenge, BayesFlow does not only give a gating which would place it among the top performers in FlowCAP I for this dataset, it also gives a more consistent treatment of different samples than either manual gating or other automated gating methods. The second real data set contains replicated flow cytometry measurements of samples from healthy individuals. BayesFlow gives here cell populations with clear expression patterns and small technical intra-donor variation as compared to biological inter-donor variation.
conclusionsModeling latent relations between samples through BayesFlow enables a systematic analysis of inter-sample variation. As opposed to other joint gating methods, effort is put at ensuring that the obtained partition of the data corresponds to actual cell populations, and the result is therefore directly biologically interpretable. BayesFlow is freely available at GitHub.
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