BMDD: A probabilistic framework for accurate imputation of zero-inflated microbiome sequencing data

Area of Science:

• Microbiome Research
• Computational Biology
• Statistical Modeling

Background:

• Microbiome sequencing data is sparse and compositional, characterized by excessive zeros.
• These zeros present significant challenges for downstream analyses, especially log-transformation.
• Existing imputation methods often fail due to assumptions of unimodal abundance.

Purpose of the Study:

• To introduce a novel probabilistic modeling framework, BiModal Dirichlet Distribution (BMDD), for accurate imputation of microbiome sequencing data.
• To address the limitations of existing methods by capturing the bimodal abundance distribution of taxa.
• To improve the analysis of zero-inflated microbiome data.

Main Methods:

• BMDD utilizes a mixture of Dirichlet priors to model bimodal abundance distributions.
• The framework employs variational inference and a scalable expectation-maximization algorithm for efficient imputation.
• The method generates multiple posterior samples to account for imputation uncertainty.

Main Results:

• BMDD demonstrated superior performance in reconstructing true abundances compared to existing methods in simulations and real datasets.
• The imputation accuracy of BMDD led to improved differential abundance analysis.
• The approach provides robust inference by managing uncertainty in zero imputation.

Conclusions:

• BMDD offers a principled and computationally efficient solution for analyzing high-dimensional, zero-inflated microbiome sequencing data.
• The method enhances microbial biomarker discovery and host-microbiome interaction studies.
• BMDD represents a significant advancement in handling sparse microbiome data.