A Bayesian Compositional Estimator for microbial taxonomy based on biomarkers
Van den Meersche, K.; Soetaert, K.; Middelburg, J.J. (2009). A Bayesian Compositional Estimator for microbial taxonomy based on biomarkers, in: Van den Meersche, K. Carbon flows in the planktonic food web of temperate estuaries: a combined approach using stable isotopes, biomarkers and modeling. pp. 51-66
In: Van den Meersche, K. (2009). Carbon flows in the planktonic food web of temperate estuaries: a combined approach using stable isotopes, biomarkers and modeling. PhD Thesis. Universiteit Gent, Vakgroep Biologie, Sectie Mariene Biologie: Gent. 199 pp., meer
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Van den Meersche, K.; Soetaert, K.; Middelburg, J.J. (2008). A Bayesian Compositional Estimator for microbial taxonomy based on biomarkers. Limnol. Oceanogr., Methods 6(5): 190-199. https://dx.doi.org/10.4319/lom.2008.6.190, meer
Determination of microbial taxonomy based on lipid or pigment spectra requires use of a compositional estimator. We present a new approach based on Bayesian inference and an implementation in the open software platform R. The Bayesian Compositional Estimator (BCE) aims not only to obtain a maximum likelihood solution, but also to provide a complete estimate of the taxonomic composition, including probability distributions and dependencies between estimated values. BCE results are compared with those obtained with CHEMTAX. The BCE has not only a similar accuracy, but also extracts more information from the data, the most obvious being standard deviation and covariance estimates.
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