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Negative binomial factor regression with application to microbiome data analysis.

Aditya K Mishra1, Christian L Müller1,2,3

  • 1Center for Computational Mathematics, Flatiron Institute, Simons Foundation, New York, New York, USA.

Statistics in Medicine
|April 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces novel statistical models to link host factors like diet and lifestyle with specific gut microbes. These methods reveal previously elusive associations, enhancing our understanding of the human microbiome.

Keywords:
American Gut Projectmicrobiomemultivariate analysisoverdispersed count datareduced rank regressionsparse singular value decomposition

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Area of Science:

  • Microbiome research
  • Statistical modeling
  • Host-microbe interactions

Background:

  • The human microbiome is crucial for host homeostasis.
  • Host factors (diet, lifestyle) influence microbial communities, but specific associations are often unclear.

Purpose of the Study:

  • To develop robust statistical models for estimating associations between host features and microbial taxa.
  • To address challenges in analyzing overdispersed amplicon sequencing data.

Main Methods:

  • Proposed negative binomial reduced rank regression (NB-RRR) and negative binomial co-sparse factor regression (NB-FAR).
  • Developed an iterative block-wise majorization procedure to solve optimization problems.
  • Applied models to American Gut Project (AGP) data.

Main Results:

  • Demonstrated the efficacy of the proposed models through simulations and AGP data analysis.
  • Identified significant links between dietary habits, host lifestyle, and specific microbial families in the AGP dataset.

Conclusions:

  • The developed factor regression models effectively identify structured associations between host factors and microbial taxa.
  • These methods provide interpretable bi-clusters, advancing the study of host-microbiome relationships.