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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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BCGLMs: Bayesian modeling for disease prediction using compositional microbiome features.

Li Zhang1, Zhenying Ding2, Nengjun Yi2

  • 1Biostatistics and Bioinformatics Facility, Fox Chase Cancer Center, Philadelphia, PA 19111, United States.

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Summary
This summary is machine-generated.

The BCGLMs R package facilitates Bayesian compositional data analysis for various response types, including microbiome data. It enhances prediction accuracy by incorporating random effects and phylogenetic relationships.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Modeling

Background:

  • Compositional data analysis is crucial for understanding complex biological systems like the microbiome.
  • Existing methods may not fully capture the nuances of microbiome data, such as phylogenetic relationships and random effects.
  • Bayesian approaches offer a flexible framework for modeling complex data structures.

Purpose of the Study:

  • To introduce BCGLMs, a novel R package for Bayesian compositional data analysis.
  • To provide tools for fitting models with various response types and incorporating random effects.
  • To enable the integration of phylogenetic information into microbiome data modeling.

Main Methods:

  • Development of the BCGLMs R package, built upon the brms package.
  • Implementation of functions for setting up and fitting Bayesian compositional generalized linear models (BCGLMs).
  • Inclusion of capabilities for handling continuous, binary, ordinal, and survival responses.
  • Integration of random effects for improved prediction accuracy.
  • Facilitation of phylogenetic relationship incorporation for microbiome taxa.

Main Results:

  • BCGLMs offers a comprehensive suite of tools for Bayesian compositional data analysis.
  • The package supports diverse response variables and advanced modeling techniques.
  • Users can leverage phylogenetic information for more accurate microbiome analysis.
  • Tools for both numerical and graphical summarization of model results are provided.

Conclusions:

  • BCGLMs provides a flexible and powerful framework for analyzing compositional microbiome data.
  • The package enhances prediction accuracy through the inclusion of random effects and phylogenetic relationships.
  • BCGLMs democratizes advanced Bayesian modeling for microbiome research.