ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2014
SWIFT-scalable clustering for automated identification of rare cell populations in large, high-dimensional flow cytometry datasets, part 1: algorithm design.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Rapid reduction in Staphylococcus aureus in atopic dermatitis subjects following dupilumab treatment.The Journal of allergy and clinical immunology · 2023Trial
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- Computationally efficient multi-sample flow cytometry data analysis using Gaussian mixture models.BMC bioinformatics · 2025Article
- Measurable (Minimal) Residual Disease in Myelodysplastic Neoplasms (MDS): Current State and Perspectives.Cancers · 2024Review
- SWIFT clustering analysis of intracellular cytokine staining flow cytometry data of the HVTN 105 vaccine trial reveals high frequencies of HIV-specific CD4+ T cell responses and associations with humoral responses.Frontiers in immunology · 2024Article
- Rarity: discovering rare cell populations from single-cell imaging data.Bioinformatics (Oxford, England) · 2023Article
- A cell-level discriminative neural network model for diagnosis of blood cancers.Bioinformatics (Oxford, England) · 2023Article
- Multiomics technologies for comprehensive tumor microenvironment analysis in triple-negative breast cancer under neoadjuvant chemotherapy.Frontiers in oncology · 2023Review
- INFLECT: an R-package for cytometry cluster evaluation using marker modality.BMC bioinformatics · 2022Article
- Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing.International journal of molecular sciences · 2022Article
- UMAP Based Anomaly Detection for Minimal Residual Disease Quantification within Acute Myeloid Leukemia.Cancers · 2022Article
- CYBERTRACK2.0: zero-inflated model-based cell clustering and population tracking method for longitudinal mass cytometry data.Bioinformatics (Oxford, England) · 2021Article
- Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches.International journal of molecular sciences · 2020Review
- Article
- Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition).European journal of immunology · 2019Article
- Flow Plex-A tool for unbiased comprehensive flow cytometry data analysis.Immunity, inflammation and disease · 2019Article
- DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2018Article
- Article
- Guidelines for the use of flow cytometry and cell sorting in immunological studies.European journal of immunology · 2017Article
- Toward deterministic and semiautomated SPADE analysis.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2017Article
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6 authors.
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Abstract
We present a model-based clustering method, SWIFT (Scalable Weighted Iterative Flow-clustering Technique), for digesting high-dimensional large-sized datasets obtained via modern flow cytometry into more compact representations that are well-suited for further automated or manual analysis. Key attributes of the method include the following: (a) the analysis is conducted in the multidimensional space retaining the semantics of the data, (b) an iterative weighted sampling procedure is utilized to maintain modest computational complexity and to retain discrimination of extremely small subpopulations (hundreds of cells from datasets containing tens of millions), and (c) a splitting and merging procedure is incorporated in the algorithm to preserve distinguishability between biologically distinct populations, while still providing a significant compaction relative to the original data. This article presents a detailed algorithmic description of SWIFT, outlining the application-driven motivations for the different design choices, a discussion of computational complexity of the different steps, and results obtained with SWIFT for synthetic data and relatively simple experimental data that allow validation of the desirable attributes. A companion paper (Part 2) highlights the use of SWIFT, in combination with additional computational tools, for more challenging biological problems.
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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.