Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1186/s12859-019-3152-3
Lizenz creative commons licence
Titel (primär) flowEMMi: an automated model-based clustering tool for microbial cytometric data
Autor Ludwig, J.; Höner zu Siederdissen, C.; Liu, Z.; Stadler, P.F.; Müller, S.
Quelle BMC Bioinformatics
Erscheinungsjahr 2019
Department UMB
Band/Volume 20
Seite von art. 643
Sprache englisch
Keywords Flow cytometry; Clustering; Data analysis; Statistical analysis; Microbial communities; Expectation-Maximization
Abstract Background: Flow 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.
Results: We 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.
Conclusions: flowEMMi 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.
dauerhafte UFZ-Verlinkung
Ludwig, J., Höner zu Siederdissen, C., Liu, Z., Stadler, P.F., Müller, S. (2019):
flowEMMi: an automated model-based clustering tool for microbial cytometric data
BMC Bioinformatics 20 , art. 643 10.1186/s12859-019-3152-3