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

[Display omitted] •Large multiparametric flow cytometry data sets are ideal for machine learning.•Unsupervised clustering methods highlight microbial community changes.•Supervised learning methods enable recognition of cell types within communities.•Machine learning-based classification correlates with taxonomic assignments.•Cell assignment probability may be a metric for phenotypic diversity. 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.