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A distance based multisample test for high-dimensional compositional data with applications to the human microbiome.

Qingyang Zhang¹, Thy Dao²

¹Department of Mathematical Sciences, University of Arkansas, Fayetteville, AR 72701, USA. qz008@uark.edu.

BMC Bioinformatics

|December 4, 2020

Summary

This summary is machine-generated.

This study introduces a new nonparametric test for analyzing compositional data, particularly useful for microbiome studies. The method effectively detects differences between populations, even with complex data structures.

Keywords:

Centered log-ratio transformation Compositional data Distance correlation High dimensionality Microbiome Multisample test

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Area of Science:

Statistics
Bioinformatics
Genomics

Background:

Compositional data, common in genomics and geology, sum to one, requiring specialized statistical methods.
Traditional statistical tests are unsuitable for compositional data due to constraints and potential over-dispersion.
Analysis of high-dimensional, over-dispersed compositional data, such as microbiome data, is challenging.

Purpose of the Study:

To develop a novel statistical test for detecting compositional differences between multiple populations.
To address the limitations of existing methods for analyzing complex, high-dimensional compositional data.
To provide a robust and efficient method for microbiome and metagenomics research.

Main Methods:

A Bayesian hypothesis formulation for compositional difference testing.
A nonparametric test utilizing inter-point distances for statistical significance.
Direct analysis of compositional data without transformations or restrictive assumptions.

Main Results:

The proposed test effectively identifies compositional differences in simulated and real microbiome data.
The method demonstrates higher sensitivity compared to mean-based approaches, especially for over-dispersed or zero-inflated data.
The test is robust, requiring no data transformation, sparsity assumption, or specific covariance matrix conditions.

Conclusions:

The developed nonparametric test is a sensitive and efficient tool for analyzing compositional differences.
Its ease of implementation and computational efficiency make it suitable for large-scale microbiome and metagenomics datasets.
The method offers a valuable alternative for researchers dealing with complex compositional data structures.