BMDD: A Probabilistic Framework for Accurate Imputation of Zero-inflated Microbiome Sequencing Data

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

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

Purpose of the Study:

• To introduce a novel probabilistic modeling framework, the BiModal Dirichlet Distribution (BMDD), for accurate imputation of microbiome sequencing data.
• To address the limitations of existing methods by accounting for the bimodal abundance distribution inherent in microbiome data.
• To enhance the accuracy of downstream analyses, including differential abundance testing and microbial biomarker discovery.

Main Methods:

• Developed BMDD, a probabilistic model utilizing a mixture of Dirichlet priors to capture bimodal abundance distributions.
• Employed variational inference and a scalable expectation-maximization algorithm for efficient imputation.
• Validated the method through simulations and analysis of real-world microbiome datasets.

Main Results:

• BMDD demonstrated superior performance in reconstructing true taxon abundances compared to existing methods.
• The imputation accuracy of BMDD led to improved results in differential abundance analyses.
• BMDD provides multiple posterior samples, enabling robust inference by quantifying imputation uncertainty.

Conclusions:

• BMDD offers a principled, computationally efficient, and accurate solution for analyzing high-dimensional, zero-inflated microbiome sequencing data.
• The method is broadly applicable to microbial biomarker discovery and host-microbiome interaction studies.
• BMDD improves the reliability and interpretability of microbiome data analysis.