A Tree-Based Model for Addressing Sparsity and Taxa Covariance in Microbiome Compositional Count Data.

Area of Science:

• Microbiology
• Computational Biology
• Statistical Modeling

Background:

• Microbiome compositional data present challenges: high dimensionality, sparsity, and significant cross-sample heterogeneity.
• Existing models often struggle to accurately represent the abundance of zeros and complex inter-taxa relationships.

Purpose of the Study:

• Introduce the logistic-tree normal (LTN) model, a novel generative approach for microbiome compositional data.
• Develop a scalable and flexible model that accounts for cross-sample heterogeneity and captures zero-abundance patterns.

Main Methods:

• The LTN model utilizes a tree-based decomposition for aggregating sparse taxa counts.
• It models relative abundances at tree splits using a multivariate logistic-normal distribution.
• Bayesian inference is enabled through efficient conjugate blocked Gibbs sampling with pólya-gamma augmentation.

Main Results:

• The LTN model demonstrates flexibility in covariance modeling among microbiome taxa.
• It effectively captures zero-inflation in compositional data by accounting for cross-sample heterogeneity, without explicit zero-inflation components.
• The model facilitates integration with existing multivariate analysis tools for high-dimensional data.

Conclusions:

• The LTN model offers a robust and scalable solution for analyzing complex microbiome compositional data.
• It provides a unified framework for differential abundance analysis, as shown in mixed-effects models.
• The LTN model validates the hypothesis that observed "zero-inflation" often stems from unaddressed cross-sample variation.