Recent advances in microbial community analysis from machine learning of multiparametric flow cytometry data.

Details

Serval ID
serval:BIB_A9FCFB45172D
Type
Article: article from journal or magazin.
Publication sub-type
Review (review): journal as complete as possible of one specific subject, written based on exhaustive analyses from published work.
Collection
Publications
Institution
Title
Recent advances in microbial community analysis from machine learning of multiparametric flow cytometry data.
Journal
Current opinion in biotechnology
Author(s)
Özel Duygan B.D., van der Meer J.R.
ISSN
1879-0429 (Electronic)
ISSN-L
0958-1669
Publication state
Published
Issued date
06/2022
Peer-reviewed
Oui
Volume
75
Pages
102688
Language
english
Notes
Publication types: Journal Article ; Review ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
Dynamic analysis of microbial composition is crucial for understanding community functioning and detecting dysbiosis. Compositional information is mostly obtained through sequencing of taxonomic markers or whole meta-genomes, which may be productively complemented by real-time quantitative community multiparametric flow cytometry data (FCM). Patterns and clusters in FCM community data can be distinguished and compared by unsupervised machine learning. Alternatively, FCM data from preselected individual strain phenotypes can be used for supervised machine-training in order to differentiate similar cell types within communities. Both types of machine learning can quantitatively deconvolute community FCM data sets and rapidly analyse global changes in response to treatment. Procedures may further be optimized for recurrent microbiome samples to simultaneously quantify physiological and compositional states.
Keywords
Flow Cytometry/methods, Machine Learning, Microbiota
Pubmed
Open Access
Yes
Create date
12/02/2022 14:50
Last modification date
18/10/2022 5:38
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