流式细胞术数据中快速的细胞群体识别。
Rapid cell population identification in flow cytometry data.
机构信息
Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada.
出版信息
Cytometry A. 2011 Jan;79(1):6-13. doi: 10.1002/cyto.a.21007.
Abstract
We have developed flowMeans, a time-efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K-means clustering. Unlike traditional K-means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. flowMeans uses a change point detection algorithm to determine the number of sub-populations, enabling the method to be used in high throughput FCM data analysis pipelines. Our approach compares favorably to manual analysis by human experts and current state-of-the-art automated gating algorithms. flowMeans is freely available as an open source R package through Bioconductor.
摘要
我们开发了 flowMeans，这是一种基于 K-均值聚类的高效、准确的流式细胞术 (FCM) 数据中自动识别细胞群体的方法。与传统的 K-均值聚类不同，flowMeans 可以通过使用多个聚类来模拟单个群体来识别凹形细胞群体。flowMeans 使用一个变化点检测算法来确定亚群的数量，从而使该方法能够用于高通量 FCM 数据分析管道。我们的方法与人类专家的手动分析和当前最先进的自动化门控算法相比具有优势。flowMeans 作为一个开放源代码的 R 包通过 Bioconductor 免费提供。
相似文献
1
Rapid cell population identification in flow cytometry data.
Cytometry A. 2011 Jan;79(1):6-13. doi: 10.1002/cyto.a.21007.
2
Critical assessment of automated flow cytometry data analysis techniques.
Nat Methods. 2013 Mar;10(3):228-38. doi: 10.1038/nmeth.2365. Epub 2013 Feb 10.
3
flowClust: a Bioconductor package for automated gating of flow cytometry data.
BMC Bioinformatics. 2009 May 14;10:145. doi: 10.1186/1471-2105-10-145.
4
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 A. 2018 Jun;93(6):597-610. doi: 10.1002/cyto.a.23371. Epub 2018 Apr 17.
5
Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data.
Cytometry A. 2016 Dec;89(12):1084-1096. doi: 10.1002/cyto.a.23030. Epub 2016 Dec 19.
6
flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification.
Bioinformatics. 2015 Feb 15;31(4):606-7. doi: 10.1093/bioinformatics/btu677. Epub 2014 Oct 16.
7
Elucidation of seventeen human peripheral blood B-cell subsets and quantification of the tetanus response using a density-based method for the automated identification of cell populations in multidimensional flow cytometry data.
Cytometry B Clin Cytom. 2010;78 Suppl 1(Suppl 1):S69-82. doi: 10.1002/cyto.b.20554.
8
Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing.
Int J Mol Sci. 2022 Mar 17;23(6):3224. doi: 10.3390/ijms23063224.
9
On extensions of k-means clustering for automated gating of flow cytometry data.
Cytometry A. 2011 Jan;79(1):3-5. doi: 10.1002/cyto.a.20988.
10
flowEMMi: an automated model-based clustering tool for microbial cytometric data.
BMC Bioinformatics. 2019 Dec 9;20(1):643. doi: 10.1186/s12859-019-3152-3.
引用本文的文献
1
Ultra-high-scale cytometry-based cellular interaction mapping.
Nat Methods. 2025 Aug 7. doi: 10.1038/s41592-025-02744-w.
2
cytoGPNet: Enhancing Clinical Outcome Prediction Accuracy Using Longitudinal Cytometry Data in Small Cohort Studies.
bioRxiv. 2025 May 7:2025.05.01.651729. doi: 10.1101/2025.05.01.651729.
3
Integrated workflow for analysis of immune enriched spatial proteomic data with IMmuneCite.
Sci Rep. 2025 Mar 19;15(1):9394. doi: 10.1038/s41598-025-93060-y.
4
Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating.
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbae633.
5
ImmCellTyper facilitates systematic mass cytometry data analysis for deep immune profiling.
Elife. 2024 Sep 6;13:RP95494. doi: 10.7554/eLife.95494.
6
Mapping the single cell spatial immune landscapes of the melanoma microenvironment.
Clin Exp Metastasis. 2024 Aug;41(4):301-312. doi: 10.1007/s10585-023-10252-4. Epub 2024 Jan 13.
7
Cytomulate: accurate and efficient simulation of CyTOF data.
Genome Biol. 2023 Nov 16;24(1):262. doi: 10.1186/s13059-023-03099-1.
8
A cell-level discriminative neural network model for diagnosis of blood cancers.
Bioinformatics. 2023 Oct 3;39(10). doi: 10.1093/bioinformatics/btad585.
9
CRUSTY: a versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data.
Nat Commun. 2023 Sep 4;14(1):5102. doi: 10.1038/s41467-023-40790-0.
10
Cross-species comparative analysis of single presynapses.
Sci Rep. 2023 Aug 24;13(1):13849. doi: 10.1038/s41598-023-40683-8.
本文引用的文献
1
Combining Mixture Components for Clustering.
J Comput Graph Stat. 2010 Jun 1;9(2):332-353. doi: 10.1198/jcgs.2010.08111.
2
Data reduction for spectral clustering to analyze high throughput flow cytometry data.
BMC Bioinformatics. 2010 Jul 28;11:403. doi: 10.1186/1471-2105-11-403.
3
Good cell, bad cell: flow cytometry reveals T-cell subsets important in HIV disease.
Cytometry A. 2010 Jul;77(7):614-22. doi: 10.1002/cyto.a.20905.
4
Data analysis in flow cytometry: the future just started.
Cytometry A. 2010 Jul;77(7):705-13. doi: 10.1002/cyto.a.20901.
5
A survey of flow cytometry data analysis methods.
Adv Bioinformatics. 2009;2009:584603. doi: 10.1155/2009/584603. Epub 2009 Dec 6.
6
Merging mixture components for cell population identification in flow cytometry.
Adv Bioinformatics. 2009;2009:247646. doi: 10.1155/2009/247646. Epub 2009 Nov 12.
7
Per-channel basis normalization methods for flow cytometry data.
Cytometry A. 2010 Feb;77(2):121-31. doi: 10.1002/cyto.a.20823.
8
Automation in high-content flow cytometry screening.
Cytometry A. 2009 Sep;75(9):789-97. doi: 10.1002/cyto.a.20754.
9
Automated high-dimensional flow cytometric data analysis.
Proc Natl Acad Sci U S A. 2009 May 26;106(21):8519-24. doi: 10.1073/pnas.0903028106. Epub 2009 May 14.
10
Toward automation of flow data analysis.
Cytometry A. 2008 Aug;73(8):679-80. doi: 10.1002/cyto.a.20617.
Zotero 插件