ArticleCommunications biology2019
Automated subset identification and characterization pipeline for multidimensional flow and mass cytometry data clustering and visualization.
Article in Communications biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Automation of flow cytometry data analysis with elastic image registration.Scientific reports · 2025Article
- Lifting the curse from high-dimensional data: automated projection pursuit clustering for a variety of biological data modalities.GigaScience · 2025Article
- A cell-level discriminative neural network model for diagnosis of blood cancers.Bioinformatics (Oxford, England) · 2023Article
- Comprehensive multi-omics single-cell data integration reveals greater heterogeneity in the human immune system.iScience · 2022Article
- A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment.Frontiers in oncology · 2022Article
- Immune cell profiling in atherosclerosis: role in research and precision medicine.Nature reviews. Cardiology · 2022Review
- CytoTree: an R/Bioconductor package for analysis and visualization of flow and mass cytometry data.BMC bioinformatics · 2021Article
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Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When examining datasets of any dimensionality, researchers frequently aim to identify individual subsets (clusters) of objects within the dataset. The ubiquity of multidimensional data has motivated the replacement of user-guided clustering with fully automated clustering. The fully automated methods are designed to make clustering more accurate, standardized and faster. However, the adoption of these methods is still limited by the lack of intuitive visualization and cluster matching methods that would allow users to readily interpret fully automatically generated clusters. To address these issues, we developed a fully automated subset identification and characterization (SIC) pipeline providing robust cluster matching and data visualization tools for high-dimensional flow/mass cytometry (and other) data. This pipeline automatically (and intuitively) generates two-dimensional representations of high-dimensional datasets that are safe from the curse of dimensionality. This new approach allows more robust and reproducible data analysis,+ facilitating the development of new gold standard practices across laboratories and institutions.
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Registered trials
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