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Activity Number:
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156
- Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
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Type:
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Contributed
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Date/Time:
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Monday, August 8, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #323372
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Title:
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Microbial Community Modeling and Diversity Estimation Using the Hierarchical Pitman-Yor Process
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Author(s):
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Kevin McGregor* and Aurelie Labbe and Celia MT Greenwood and Todd Parsons and Christopher Quince
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Companies:
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York University and HEC Montreal and McGill University and Sorbonne University and Earlham Institute
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Keywords:
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Bayesian non-parametrics;
Microbiome;
Pitman-Yor;
Diversity;
Hill numbers
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Abstract:
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The human microbiome comprises the microorganisms that inhabit the human body. The composition of a microbial population is often quantified through measures of species diversity, which summarize the number of species along with their relative abundances into a single value. In a microbiome sample there will certainly be species missing from the target population which will affect the diversity estimates. We employ a model based on the hierarchical Pitman-Yor (HPY) process to model the species abundance distributions over multiple populations. The model parameters are estimated using a Gibbs sampler. We also derive estimates of species diversity, conditional and unconditional on the observed data, as a function of the HPY parameters. Finally, we derive a general formula for the Hill numbers in the HPY context. We show that the Gibbs sampler for the HPY model performs well in simulations. We also show that the conditional estimates of diversity from the HPY model improve over naive estimates when species are missing. Similarly the conditional HPY estimates tend to perform better than the naive estimates especially when the number of individuals sampled from a population is small.
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Authors who are presenting talks have a * after their name.
