Journal of Advanced Statistics
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: December 15, 2019
Author(s)
- Toni Monleón-Getino*
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 - Clara I Rodríguez-Casado
Section of Statistics. Department of Genetics, Microbiology and Statistics. University of Barcelona, Barcelona, Spain; BIOST3. Research Group in Clinical Statistics, Bioinformatics and Computacional Biodiversity - Pablo Emilio Verde
Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
Abstract
Keywords
References
[1] C. M. Guinane and P. D. Cotter. “Role of the gut microbiota in health and chronic gastrointestinal disease: understanding a hidden metabolic organ”, Therapeutic advances in gastroenterology, vol. 6, no. 4, pp. 295–308, 2013.
[2] M. Pollan. “Some of My Best Friends Are Germs”. 2013. Available: http://www.nytimes.com/2013/ 05/19/magazine/say-hello-to-the-100-trillion-bacteria-that-make-up-your-microbiome.html?_r=1
[3] J. Handelsman. “Metagenomics: Application of Genomics to Uncultured Microorganisms”. Microbiology and Molecular Biology Review, vol. 68, no. 4, pp: 669–685, 2004.
[4] CI. Rodríguez and T. Monleón-Getino. “A new R library for discriminating groups based on abundance profile and biodiversity in microbiome metagenomic matrices”. International Journal of Scientific and Engineering Research, vol. 7, no. 10, pp: 243-253, 2016.
[5] D. Marco (editor). “Metagenomics: Current Innovations and Future Trends”. Caister Academic Press, 2011.
[6] B. J. M. Bohannan and J. Hughesy. “New approaches to analyzing microbial biodiversity data” Current Opinion in Microbiology, vol. 6, pp: 282–287. 2003. Available: https://pages.uoregon.edu/bohannanlab/pubs/ Bohannan%20and%20Hughes03%20copy.pdf
[7] C. E. Shannon. "A Mathematical Theory of Communication". Bell System Technical Journal (PDF), vol. 27, no. 3, pp: 379–423, 1948.
[8] WD. Wadsworth, R. Argiento, M. Guindani, J. Galloway-Pena, SA. Shelbourne and M. Vannucci. “An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data”. BMC Bioinformatics, vol, 18, no, 94. 2017.
[9] J. R. Doroghazi and D. H. Buckley. “Evidence from GC-TRFLP that Bacterial Communities in Soil Are Lognormally Distributed”. PLoS One, vol. 3 no. 8, pp. e2910, 2008.
[10] R. A. Fisher, Corbet A. S., and C. B. Williams. “The relation between the number of species and the number of individuals in a random sample of an animal population”. Journal of Animal Ecology, vol. 12, pp. 42–58, 1943.
[11] D. J. Golichier, R. B. O’Hara, L. Ruíz-M, and L. Cayuela. “Lifting a veil on diversity: a Bayesian approach to fitting relative-abundance models”. Ecological Applications, vol. 16, no. 1, pp. 202–212, 2006
[12] S. D. Hooper, D. Dalevi, A. Pati, K. Mavromatis, N. N. Ivanova and N. C. Kyrpides. “Estimating DNA coverage and abundance in metagenomes using a Gamma approximation”. Bioinformatics, vol. 26, pp. 295–301, 2010.
[13] JA. Royle “N-mixture models for estimating population size from spatially replicated counts”. Biometrics, vol. 60, no. 1: pp. 108-115, 2004.
[14] M.S. Lindner and B. Y. Renard. “Metagenomic abundance estimation and diagnostic testing on species level”. Nucleic Acids research, vol. 41 n. 1, pp. e10, 2012.
[15] I. Holmes, K. Harris and C. Quince. “Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics”. PLoS ONE, vol. 7 no. 2, 2012.
[16] P. Pizarro. “Bacterial Metagenomics: Associated Probability Distributions and Profile Analysis”. Master thesis of the master in Biostatistics and Bioinformatics (UOC-OPC, Barcelona, Spain). Adviced by Toni Monleón Getino. 2016
[17] B. S. Kim and B. H. Margolin. “Testing Goodness of Fit of a Multinomial Model Against Overdispersed Alternatives”. Biometrics, vol, 48, pp. 711-719, 1992.
[18] A.A. Niane, M. Singh and P. C. Strulk. “Bayesian estimation of shrubs diversity in rangelands under two management systems in northern Syria”. Open Journal of Ecology, vol. 4, pp. 163-173, 2004
[19] D. Lunn, D. Spiegelhalter, A. Thomas and N. Best. “The BUGS project: Evolution, critique and future directions”. Statistics in Medicine, vol. 28, pp. 3049-67, 2009.
[20] M. A. McCarthy. Bayesian Methods for Ecology”. Cambridge University Press, 2007.
[21] M. Plummer “JAGS: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling”. Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003). March 20–22. Vienna, Austria, 2003.
[22] R Core Team. “R: A language and environment for statistical computing”. R Foundation for Statistical Computing. Vienna. Austria. 2016. Available: http://www.R-project.org/.
[23] CI. Rodríguez, T. Monleón-Getino, M. Cubedo, M. Ríos-Alcolea. “A priori groups based on Bhattacharyya distance and partitioning around medoids (PAM) with applications to metagenomics”. IOSR Journal of Mathematics, vol. 13, no. 3, pp. 24-32, 2017.
[24] A. Monleon-Getino, CI. Rodríguez-Casado and J. Méndez-Viera. “Sample size in metagenomics. a bayesian approach using BDSbiost3 for R”. XVI Spanish Biometric Conference. CEB, Sevilla, Spain, 2017. Library for R BDSbiost3, avalaible at: at: https://github.com/amonleong/BDSbiost3
[25] J. K. Kruschke. “Doing Bayesian Data Analysis A Tutorial with R. JAGS. and Stan”. Academic Press / Elsevier, 2015.