Given a dataset of B cell subpopulation quantities, for aboutsix thousand patients, that is a cross-sectional immunological dataset,here we detect clusters representing models of immune system states inan unsupervised way (i.e., according only to their different statisticalproperties). Two time-evolving B cell networks are also generated fromdata-driven hidden Markov models, with four and five hidden states,respectively. Our interpretation from a biomedical viewpoint of the sta-tistical parameters of the Bayesian models confirms an age related declineof some types of B cell functions and finds out a class of old patients withunexpected B cell values.

Bayesian clustering of multivariate immunological data

A. Castellini
;
G. Franco
2019-01-01

Abstract

Given a dataset of B cell subpopulation quantities, for aboutsix thousand patients, that is a cross-sectional immunological dataset,here we detect clusters representing models of immune system states inan unsupervised way (i.e., according only to their different statisticalproperties). Two time-evolving B cell networks are also generated fromdata-driven hidden Markov models, with four and five hidden states,respectively. Our interpretation from a biomedical viewpoint of the sta-tistical parameters of the Bayesian models confirms an age related declineof some types of B cell functions and finds out a class of old patients withunexpected B cell values.
978-3-030-13708-3
Immunosenescence, data analysis, clustering, Hidden Markov Models, time series analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1002926
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