It's all relative: analyzing microbiome data as compositions.

Area of Science:

• Microbiology
• Bioinformatics
• Statistical analysis

Background:

• High-throughput sequencing generates large microbiome datasets, but analysis tools lag behind data acquisition capabilities.
• 16S rRNA gene sequencing data are compositional, existing analysis tools often fail to account for this, leading to potential misinterpretations.
• The natural sample space for sequencing data is a simplex, requiring nonnegative values with a constant sum, a characteristic often ignored.

Purpose of the Study:

• To adapt existing compositional data (CoDa) analysis tools for high-throughput microbiome sequencing data.
• To demonstrate the efficacy of CoDa in analyzing complex microbiome datasets.
• To provide a robust framework for accurate microbiome data interpretation.

Main Methods:

• Utilized established compositional data analysis (CoDa) methodologies.
• Applied CoDa tools to analyze the Human Microbiome Project tongue versus buccal mucosa dataset.
• Reanalyzed a public autism microbiome dataset using CoDa with multiple hypothesis test corrections.

Main Results:

• The CoDa approach effectively addresses key aspects of microbiome analysis, as shown with the Human Microbiome Project dataset.
• Reanalysis of the autism microbiome dataset revealed that CoDa, combined with hypothesis testing corrections, successfully prevents false positive findings.
• The CoDa approach is scalable for large-scale microbiome analyses.

Conclusions:

• Compositional data analysis (CoDa) provides a suitable framework for analyzing microbiome sequencing data.
• CoDa enhances the accuracy of microbiome data interpretation and reduces the risk of false positives.
• Recommendations and example code are provided to facilitate improved microbiome data analysis and reporting.