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Journal of Advanced Statistics
JAS > Volume 4, Number 4, December 2019

Shannon Entropy Ratio, a Bayesian Biodiversity Index Used in the Uncertainty Mixtures of Metagenomic Populations

Download PDF  (586.3 KB)PP. 23-34,  Pub. Date:November 7, 2019
DOI: 10.22606/jas.2019.44001

Author(s)
Toni Monleón-Getino, Clara I Rodríguez-Casado, Pablo Emilio Verde
Affiliation(s)
Section of Statistics. Department of Genetics, Microbiology and Statistics. University of Barcelona, Barcelona, Spain; GRBIO. Research Group in Biostatistics and Bioinformatics; BIOST3. Research Group in Clinical Statistics, Bioinformatics and Computacional Biodiversity
Section of Statistics. Department of Genetics, Microbiology and Statistics. University of Barcelona, Barcelona, Spain; BIOST3. Research Group in Clinical Statistics, Bioinformatics and Computacional Biodiversity
Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
Abstract
The microbial communities contain a unique complexity that makes difficult studying their diversity. Unfortunately the culture of microorganisms is very complex for their identification and is necessary study the microbial communities sampled directly from their natural environment using metagenomic approach. An important problem in metagenomics is measuring the diversity in a population (entropy) and the variation between subpopulations (beta-diversity) in uncertainty conditions. A good method that we propose can be use the Bayesian Shannon index and Shannon entropy ratio (SER) to estimate it, using a prior information based on a phylogenetic previously estimation. Bayesian methods improve the precision of parameter estimates, and uncertainty in parameter estimates can be easily propagated in calculations. The Bayesian diversity estimates were higher than their frequentist counterparts and had lower standard errors, so this approximation is present the diversity mixed index by means Markov Chain Monte Carlo (MCMC) simulation using JAGS with R.
Keywords
Entropy, Bayesian methods, biodiversity, probability, categorical data, metagenomics, microbiology.
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