Sparse Treatment-Effect Model for Taxon Identification with High-Dimensional Metagenomic Data

Area of Science:

• Metagenomics
• Microbiome analysis
• Statistical inference

Background:

• Identifying disease-associated taxa is crucial in metagenomics.
• Existing methods often neglect taxon interactions or lack P-value generation.
• There's a need for methods that combine interaction analysis with statistical significance.

Purpose of the Study:

• To introduce a novel statistical model, STEMIT (STatistical Effective Model for Identifying Taxa), for high-dimensional metagenomic data.
• To enable identification of disease-associated taxa while considering their interactions.
• To provide P-values for statistical inference and potential causal inference in clinical trials.

Main Methods:

• STEMIT utilizes a two-step treatment-effect maximization approach.
• Sparse modeling identifies taxa associated with treatment-effect and target features.
• Ordinary Least Squares (OLS) regression estimates the P-value for the target taxon.

Main Results:

• The proposed STEMIT method demonstrates efficiency in identifying important taxa.
• The method was successfully applied to a real-world metagenomic dataset.
• STEMIT provides P-values, enabling robust statistical inference.

Conclusions:

• STEMIT offers a significant advancement in analyzing high-dimensional metagenomic data.
• The model effectively identifies disease-associated taxa by accounting for interactions.
• STEMIT facilitates deeper insights into microbiome-disease relationships and supports causal inference.