Affordable Access

deepdyve-link
Publisher Website

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

Authors
  • Rubbens, Peter
  • Props, Ruben
  • Kerckhof, Frederiek-Maarten
  • Boon, Nico
  • Waegeman, Willem
Publication Date
Jan 01, 2021
Identifiers
DOI: 10.1128/msphere.00530-20
OAI: oai:archive.ugent.be:8700141
Source
Ghent University Institutional Archive
Keywords
Language
English
License
Unknown
External links

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.

Report this publication

Statistics

Seen <100 times