ArticleBMC systems biology2019
Ultrafast clustering of single-cell flow cytometry data using FlowGrid.
Article in BMC systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 40 citations in OpenAlex.
- CARGO: A Cytometry Analysis framework via Regularized Graph Optimal-transport.PLoS computational biology · 2026Article
- Automatic computational classification of bone marrow cells for B cell pediatric leukemia using UMAP.BioData mining · 2025Article
- Machine Learning Methods in Clinical Flow Cytometry.Cancers · 2025Review
- Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating.Briefings in bioinformatics · 2024Article
- CyCadas: accelerating interactive annotation and analysis of clustered cytometry data.Bioinformatics (Oxford, England) · 2024Article
- Efficient sex separation by exploiting differential alternative splicing of a dominant marker in Aedes aegypti.PLoS genetics · 2023Article
- INFLECT: an R-package for cytometry cluster evaluation using marker modality.BMC bioinformatics · 2022Article
- Flow Cytometry: A Blessing and a Curse.Biomedicines · 2021Article
- Single-Cell Multiomics Analysis for Drug Discovery.Metabolites · 2021Review
- Analyzing high-dimensional cytometry data using FlowSOM.Nature protocols · 2021Review
- MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning.International journal of molecular sciences · 2021Article
- K-means quantization for a web-based open-source flow cytometry analysis platform.Scientific reports · 2021Article
- Optimal distribution-preserving downsampling of large biomedical data sets (opdisDownsampling).PloS one · 2021Article
- Biophysical Review's 'meet the editors series'-a profile of Joshua W. K. Ho.Biophysical reviews · 2020Article
- Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study.International journal of molecular sciences · 2020Article
- Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data.Frontiers in cell and developmental biology · 2020Review
- Flow Cytometric Analyses of Lymphocyte Markers in Immune Oncology: A Comprehensive Guidance for Validation Practice According to Laws and Standards.Frontiers in immunology · 2020Article
- Article
- Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow.Micromachines · 2019Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFlow cytometry is a popular technology for quantitative single-cell profiling of cell surface markers. It enables expression measurement of tens of cell surface protein markers in millions of single cells. It is a powerful tool for discovering cell sub-populations and quantifying cell population heterogeneity. Traditionally, scientists use manual gating to identify cell types, but the process is subjective and is not effective for large multidimensional data. Many clustering algorithms have been developed to analyse these data but most of them are not scalable to very large data sets with more than ten million cells.
resultsHere, we present a new clustering algorithm that combines the advantages of density-based clustering algorithm DBSCAN with the scalability of grid-based clustering. This new clustering algorithm is implemented in python as an open source package, FlowGrid. FlowGrid is memory efficient and scales linearly with respect to the number of cells. We have evaluated the performance of FlowGrid against other state-of-the-art clustering programs and found that FlowGrid produces similar clustering results but with substantially less time. For example, FlowGrid is able to complete a clustering task on a data set of 23.6 million cells in less than 12 seconds, while other algorithms take more than 500 seconds or get into error.
conclusionsFlowGrid is an ultrafast clustering algorithm for large single-cell flow cytometry data. The source code is available at https://github.com/VCCRI/FlowGrid .
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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.