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PhenoGMM : Gaussian mixture modeling of cytometry data quantifies changes in microbial community structure

Peter Rubbens, Ruben Props (UGent) , Frederiek-Maarten Kerckhof (UGent) , Nico Boon (UGent) and Willem Waegeman (UGent)
(2021) MSPHERE. 6(1).
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Abstract
Microbial flow cytometry can rapidly characterize the status of microbial communities. Upon measurement, large amounts of quantitative single-cell data are generated, which need to be analyzed appropriately. Cytometric fingerprinting approaches are often used for this purpose. Traditional approaches either require a manual annotation of regions of interest, do not fully consider the multivariate characteristics of the data, or result in many community-describing variables. To address these shortcomings, we propose an automated model-based fingerprinting approach based on Gaussian mixture models, which we call PhenoGMM. The method successfully quantifies changes in microbial community structure based on flow cytometry data, which can be expressed in terms of cytometric diversity. We evaluate the performance of PhenoGMM using data sets from both synthetic and natural ecosystems and compare the method with a generic binning fingerprinting approach. PhenoGMM supports the rapid and quantitative screening of microbial community structure and dynamics.
Keywords
diversity, fingerprint, flow cytometry, machine learning, microbial communities, mixture model, FLOW-CYTOMETRY, CLUSTERING ALGORITHMS, DIVERSITY, DYNAMICS, BACTERIOPLANKTON, PATTERNS, PHYTOPLANKTON, HETEROGENEITY, FINGERPRINTS, TOOLS

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MLA
Rubbens, Peter, et al. “PhenoGMM : Gaussian Mixture Modeling of Cytometry Data Quantifies Changes in Microbial Community Structure.” MSPHERE, vol. 6, no. 1, 2021, doi:10.1128/msphere.00530-20.
APA
Rubbens, P., Props, R., Kerckhof, F.-M., Boon, N., & Waegeman, W. (2021). PhenoGMM : Gaussian mixture modeling of cytometry data quantifies changes in microbial community structure. MSPHERE, 6(1). https://doi.org/10.1128/msphere.00530-20
Chicago author-date
Rubbens, Peter, Ruben Props, Frederiek-Maarten Kerckhof, Nico Boon, and Willem Waegeman. 2021. “PhenoGMM : Gaussian Mixture Modeling of Cytometry Data Quantifies Changes in Microbial Community Structure.” MSPHERE 6 (1). https://doi.org/10.1128/msphere.00530-20.
Chicago author-date (all authors)
Rubbens, Peter, Ruben Props, Frederiek-Maarten Kerckhof, Nico Boon, and Willem Waegeman. 2021. “PhenoGMM : Gaussian Mixture Modeling of Cytometry Data Quantifies Changes in Microbial Community Structure.” MSPHERE 6 (1). doi:10.1128/msphere.00530-20.
Vancouver
1.
Rubbens P, Props R, Kerckhof F-M, Boon N, Waegeman W. PhenoGMM : Gaussian mixture modeling of cytometry data quantifies changes in microbial community structure. MSPHERE. 2021;6(1).
IEEE
[1]
P. Rubbens, R. Props, F.-M. Kerckhof, N. Boon, and W. Waegeman, “PhenoGMM : Gaussian mixture modeling of cytometry data quantifies changes in microbial community structure,” MSPHERE, vol. 6, no. 1, 2021.
@article{8700141,
  abstract     = {{Microbial flow cytometry can rapidly characterize the status of microbial communities. Upon measurement, large amounts of quantitative single-cell data are generated, which need to be analyzed appropriately. Cytometric fingerprinting approaches are often used for this purpose. Traditional approaches either require a manual annotation of regions of interest, do not fully consider the multivariate characteristics of the data, or result in many community-describing variables. To address these shortcomings, we propose an automated model-based fingerprinting approach based on Gaussian mixture models, which we call PhenoGMM. The method successfully quantifies changes in microbial community structure based on flow cytometry data, which can be expressed in terms of cytometric diversity. We evaluate the performance of PhenoGMM using data sets from both synthetic and natural ecosystems and compare the method with a generic binning fingerprinting approach. PhenoGMM supports the rapid and quantitative screening of microbial community structure and dynamics.}},
  articleno    = {{e00530-20}},
  author       = {{Rubbens, Peter and Props, Ruben and Kerckhof, Frederiek-Maarten and Boon, Nico and Waegeman, Willem}},
  issn         = {{2379-5042}},
  journal      = {{MSPHERE}},
  keywords     = {{diversity,fingerprint,flow cytometry,machine learning,microbial communities,mixture model,FLOW-CYTOMETRY,CLUSTERING ALGORITHMS,DIVERSITY,DYNAMICS,BACTERIOPLANKTON,PATTERNS,PHYTOPLANKTON,HETEROGENEITY,FINGERPRINTS,TOOLS}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{15}},
  title        = {{PhenoGMM : Gaussian mixture modeling of cytometry data quantifies changes in microbial community structure}},
  url          = {{http://doi.org/10.1128/msphere.00530-20}},
  volume       = {{6}},
  year         = {{2021}},
}
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