ArticleBioinformatics (Oxford, England)2012
flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.
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65 citing papers in PubMed.
- Multi-Dimensional Flow Cytometry Analyses Reveal a Dichotomous Role for Nitric Oxide in Melanoma Patients Receiving Immunotherapy.Frontiers in immunology · 2020Trial
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- cytoGPNet: Enhancing clinical outcome prediction accuracy using longitudinal cytometry data in small cohort studies.Patterns (New York, N.Y.) · 2025Article
- Functionality and phenotype of T cells in patients with varying severity of acute dengue and metabolic status.medRxiv : the preprint server for health sciences · 2025Article
- Polytect: an automatic clustering and labeling method for multicolor digital PCR data.NAR genomics and bioinformatics · 2025Article
- Machine Learning Methods in Clinical Flow Cytometry.Cancers · 2025Review
- A cytometric framework to assess trends in the morphological structure of bacterioplankton communities along freshwater environmental gradients.ISME 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
- AutoGater: a weakly supervised neural network model to gate cells in flow cytometric analyses.Scientific reports · 2024Article
- Benchmarking digital PCR partition classification methods with empirical and simulated duplex data.Briefings in bioinformatics · 2024Article
- Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach.Proteomics · 2023Article
- A cell-level discriminative neural network model for diagnosis of blood cancers.Bioinformatics (Oxford, England) · 2023Article
- CRUSTY: a versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data.Nature communications · 2023Article
- Statistical and machine learning methods for immunoprofiling based on single-cell data.Human vaccines & immunotherapeutics · 2023Review
- Influence of Varying Pre-Culture Conditions on the Level of Population Heterogeneity in Batch Cultures with anMicroorganisms · 2023Article
- Non-inhibitory levels of oxygen during cultivation increase freeze-drying stress tolerance inFrontiers in microbiology · 2023Article
- INFLECT: an R-package for cytometry cluster evaluation using marker modality.BMC bioinformatics · 2022Article
- Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity.Nature communications · 2022Article
- Advances in automated real-time flow cytometry for monitoring of bioreactor processes.Engineering in life sciences · 2022Review
- Searching for a technology-driven acute rheumatic fever test: the START study protocol.BMJ open · 2021Article
5 more citing papers are in PubMed but not listed here.
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2 authors.
Funding
Abstract
motivationFor flow cytometry data, there are two common approaches to the unsupervised clustering problem: one is based on the finite mixture model and the other on spatial exploration of the histograms. The former is computationally slow and has difficulty to identify clusters of irregular shapes. The latter approach cannot be applied directly to high-dimensional data as the computational time and memory become unmanageable and the estimated histogram is unreliable. An algorithm without these two problems would be very useful.
resultsIn this article, we combine ideas from the finite mixture model and histogram spatial exploration. This new algorithm, which we call flowPeaks, can be applied directly to high-dimensional data and identify irregular shape clusters. The algorithm first uses K-means algorithm with a large K to partition the cell population into many small clusters. These partitioned data allow the generation of a smoothed density function using the finite mixture model. All local peaks are exhaustively searched by exploring the density function and the cells are clustered by the associated local peak. The algorithm flowPeaks is automatic, fast and reliable and robust to cluster shape and outliers. This algorithm has been applied to flow cytometry data and it has been compared with state of the art algorithms, including Misty Mountain, FLOCK, flowMeans, flowMerge and FLAME. AVAILABILITY: The R package flowPeaks is available at https://github.com/yongchao/flowPeaks. CONTACT: yongchao.ge@mssm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.