Identifying unmeasured heterogeneity in microbiome data via quantile thresholding (QuanT)

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• High-throughput microbiome data exhibit technical heterogeneity from experimental variations.
• Unmeasured factors cause spurious conclusions, a growing issue in multi-center studies.
• Existing methods for RNA-seq data fail to address microbiome data's sparsity and over-dispersion.

Purpose of the Study:

• Introduce a novel non-parametric approach for identifying unmeasured heterogeneity in microbiome data.
• Develop a tool tailored to the unique characteristics of microbiome datasets.
• Enhance the accuracy and reliability of microbiome data analysis.

Main Methods:

• Quantile thresholding (QuanT) uses quantile regression across multiple levels.
• Microbiome abundance data are thresholded to uncover latent heterogeneity.
• Thresholded binary residual matrices are generated for analysis.

Main Results:

• QuanT effectively identifies and mitigates unmeasured heterogeneity in microbiome data.
• Validation on synthetic and real datasets demonstrates QuanT's superiority.
• Improved accuracy in downstream analyses including prediction, differential abundance, and diversity evaluations.

Conclusions:

• QuanT is a novel tool for comprehensive identification of unmeasured heterogeneity in microbiome data.
• The non-parametric method significantly enhances downstream analyses.
• QuanT serves as a valuable tool for microbiome data integration and analysis.