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flowEMMi: an automated model-based clustering tool for microbial cytometric data

Authors
  • Ludwig, Joachim1
  • zu Siederdissen, Christian Höner2
  • Liu, Zishu1
  • Stadler, Peter F.2
  • Müller, Susann1
  • 1 Department of Environmental Microbiology, Research Group Flow Cytometry, Helmholtz Centre for Environmental Research, Permoserstraße 15, Leipzig, 04318, Germany , Leipzig (Germany)
  • 2 Department of Computer Science, University Leipzig, Härtelstr. 16-18, Leipzig, 04107, Germany , Leipzig (Germany)
Type
Published Article
Journal
BMC Bioinformatics
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Dec 09, 2019
Volume
20
Issue
1
Identifiers
DOI: 10.1186/s12859-019-3152-3
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundFlow cytometry (FCM) is a powerful single-cell based measurement method to ascertain multidimensional optical properties of millions of cells. FCM is widely used in medical diagnostics and health research. There is also a broad range of applications in the analysis of complex microbial communities. The main concern in microbial community analyses is to track the dynamics of microbial subcommunities. So far, this can be achieved with the help of time-consuming manual clustering procedures that require extensive user-dependent input. In addition, several tools have recently been developed by using different approaches which, however, focus mainly on the clustering of medical FCM data or of microbial samples with a well-known background, while much less work has been done on high-throughput, online algorithms for two-channel FCM.ResultsWe bridge this gap with flowEMMi, a model-based clustering tool based on multivariate Gaussian mixture models with subsampling and foreground/background separation. These extensions provide a fast and accurate identification of cell clusters in FCM data, in particular for microbial community FCM data that are often affected by irrelevant information like technical noise, beads or cell debris. flowEMMi outperforms other available tools with regard to running time and information content of the clustering results and provides near-online results and optional heuristics to reduce the running-time further.ConclusionsflowEMMi is a useful tool for the automated cluster analysis of microbial FCM data. It overcomes the user-dependent and time-consuming manual clustering procedure and provides consistent results with ancillary information and statistical proof.

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