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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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A logistic normal multinomial regression model for microbiome compositional data analysis.

Fan Xia1, Jun Chen, Wing Kam Fung

  • 1Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam, Hong Kong.

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|October 17, 2013
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Summary
This summary is machine-generated.

This study introduces a new statistical model for analyzing human microbiome data, linking bacterial composition to environmental factors. The method effectively identifies key nutrients associated with gut microbiome enterotypes.

Keywords:
Hierarchical modelMarkov chain Monte CarloOver-dispersionRegularizationVariable selection

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Human microbiome alterations correlate with numerous diseases.
  • Next-generation sequencing enables microbial quantification without cultivation.
  • Identifying environmental factors linked to bacterial taxa is crucial for microbiome data analysis.

Purpose of the Study:

  • To develop a robust statistical model for associating covariates with bacterial composition in microbiome data.
  • To address challenges of over-dispersion and zero inflation in microbiome count data.
  • To enable accurate variable selection and estimation of regression coefficients.

Main Methods:

  • Additive logistic normal multinomial regression model to handle over-dispersion and zero counts.
  • Group ℓ1 penalized likelihood estimation for covariate selection and coefficient estimation.
  • Monte Carlo Expectation-Maximization algorithm for implementing the penalized likelihood estimation.

Main Results:

  • The proposed model effectively accounts for sampling variability and zero observations.
  • Simulation studies indicate superior performance in variable selection compared to existing methods.
  • The method successfully identified micro-nutrients associated with human gut microbiome enterotypes in a real-world dataset.

Conclusions:

  • The additive logistic normal multinomial regression with group ℓ1 penalization offers a powerful approach for microbiome data analysis.
  • This method enhances the identification of environmental and biological factors influencing the microbiome.
  • The findings have implications for understanding host-microbiome interactions and disease associations.