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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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flowEMMi: an automated model-based clustering tool for microbial cytometric data.

Joachim Ludwig1, Christian Höner Zu Siederdissen2, Zishu Liu1

  • 1Department of Environmental Microbiology, Research Group Flow Cytometry, Helmholtz Centre for Environmental Research, Permoserstraße 15, Leipzig, 04318, Germany.

BMC Bioinformatics
|December 10, 2019
PubMed
Summary
This summary is machine-generated.

flowEMMi automates microbial community analysis using flow cytometry (FCM) data. This new tool provides fast, accurate, and consistent clustering, overcoming limitations of manual methods for tracking microbial dynamics.

Keywords:
ClusteringData analysisExpectation-MaximizationFlow cytometryMicrobial communitiesStatistical analysis

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Area of Science:

  • Microbiology
  • Computational Biology
  • Biotechnology

Background:

  • Flow cytometry (FCM) is a key technology for analyzing cell properties, with broad applications in medical diagnostics and microbial community research.
  • Tracking microbial subcommunity dynamics in complex samples is crucial but often relies on time-consuming manual clustering.
  • Existing computational tools often focus on medical FCM data or specific microbial scenarios, with limited high-throughput, online algorithms for two-channel FCM.

Purpose of the Study:

  • To develop a fast, accurate, and automated clustering tool for microbial flow cytometry data.
  • To address the need for high-throughput, online algorithms in microbial community analysis.
  • To overcome the limitations of manual, user-dependent clustering procedures.

Main Methods:

  • Developed flowEMMi, a model-based clustering tool utilizing multivariate Gaussian mixture models.
  • Incorporated subsampling and foreground/background separation for enhanced accuracy and speed.
  • Implemented optional heuristics for further running-time reduction.

Main Results:

  • flowEMMi enables fast and accurate identification of cell clusters in FCM data, particularly for microbial communities.
  • The tool effectively handles irrelevant information such as noise, beads, and cell debris.
  • flowEMMi demonstrates superior performance over existing tools in terms of running time and clustering result information content, providing near-online results.

Conclusions:

  • flowEMMi offers a valuable solution for automated cluster analysis of microbial FCM data.
  • The tool significantly reduces the time and user dependency associated with manual clustering.
  • flowEMMi provides consistent, statistically validated results with ancillary information for microbial community studies.