ArticleBMC bioinformatics2009
flowClust: a Bioconductor package for automated gating of flow cytometry data.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.
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Who cites it
92 citing papers in PubMed, 199 citations in OpenAlex.
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- SingletSeeker: An unsupervised clustering approach for automated singlet discrimination in cytometry.Cytometry. Part B, Clinical cytometry · 2025Article
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- Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating.Briefings in bioinformatics · 2024Article
- Benchmarking digital PCR partition classification methods with empirical and simulated duplex data.Briefings in bioinformatics · 2024Article
- CytoPipeline and CytoPipelineGUI: a Bioconductor R package suite for building and visualizing automated pre-processing pipelines for flow cytometry data.BMC bioinformatics · 2024Article
- Long non-coding RNA GRASLND links melanoma differentiation and interferon-gamma response.Frontiers in molecular biosciences · 2024Article
- Active compensation for changes in TDH3 expression mediated by direct regulators of TDH3 in Saccharomyces cerevisiae.PLoS genetics · 2023Article
- Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach.Proteomics · 2023Article
- Computational Methods for Single-Cell Proteomics.Annual review of biomedical data science · 2023Review
- A Bayesian feature allocation model for identifying cell subpopulations using CyTOF data.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- Manipulation of topoisomerase expression inhibits cell division but not growth and reveals a distinctive promoter structure in Synechocystis.Nucleic acids research · 2022Article
- Optochemical Control of TET Dioxygenases Enables Kinetic Insights into the Domain-Dependent Interplay of TET1 and MBD1 while Oxidizing and Reading 5-Methylcytosine.ACS chemical biology · 2022Article
32 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundAs a high-throughput technology that offers rapid quantification of multidimensional characteristics for millions of cells, flow cytometry (FCM) is widely used in health research, medical diagnosis and treatment, and vaccine development. Nevertheless, there is an increasing concern about the lack of appropriate software tools to provide an automated analysis platform to parallelize the high-throughput data-generation platform. Currently, to a large extent, FCM data analysis relies on the manual selection of sequential regions in 2-D graphical projections to extract the cell populations of interest. This is a time-consuming task that ignores the high-dimensionality of FCM data.
resultsIn view of the aforementioned issues, we have developed an R package called flowClust to automate FCM analysis. flowClust implements a robust model-based clustering approach based on multivariate t mixture models with the Box-Cox transformation. The package provides the functionality to identify cell populations whilst simultaneously handling the commonly encountered issues of outlier identification and data transformation. It offers various tools to summarize and visualize a wealth of features of the clustering results. In addition, to ensure its convenience of use, flowClust has been adapted for the current FCM data format, and integrated with existing Bioconductor packages dedicated to FCM analysis.
conclusionflowClust addresses the issue of a dearth of software that helps automate FCM analysis with a sound theoretical foundation. It tends to give reproducible results, and helps reduce the significant subjectivity and human time cost encountered in FCM analysis. The package contributes to the cytometry community by offering an efficient, automated analysis platform which facilitates the active, ongoing technological advancement.
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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.