Compositional data analysis of the microbiome: fundamentals, tools, and challenges.

Area of Science:

• Microbiology
• Bioinformatics
• Statistical Analysis

Background:

• Human microbiome studies generate compositional data, where only relative abundances of microbes are known.
• Traditional statistical methods applied to compositional data can yield misleading results due to inherent constraints.

Purpose of the Study:

• To review the origins of compositionality in microbiome data.
• To discuss the theory and application of compositional data analysis (CoDA) in microbiome research.
• To explore recent advancements and challenges in CoDA for microbiome studies.

Main Methods:

• Review of existing literature on compositional data analysis.
• Theoretical discussion of CoDA principles applied to high-dimensional, sparse microbiome datasets.
• Analysis of challenges arising from the interplay of compositionality, high dimensionality, and sparsity.

Main Results:

• Compositionality in microbiome data arises from the nature of sequencing and relative abundance measurements.
• High dimensionality and sparsity of microbiome data exacerbate analytical challenges when using CoDA.
• Current CoDA approaches may still present limitations for robust microbiome data inference.

Conclusions:

• Further research is needed for improved simulation of microbiome data.
• Investigating the impact of varying microbial community diversity on compositional effects is crucial.
• Developing more robust statistical frameworks for analyzing compositional microbiome data is essential for advancing the field.