Identification of microbiome markers for ordered groups.

Area of Science:

• Microbiome research
• Statistical bioinformatics
• Computational biology

Background:

• Microbiome markers linked to ordered phenotypes are vital for understanding disease progression and precision medicine.
• Current differential abundance methods often fail to capture trends across ordered categories due to binary comparisons.

Purpose of the Study:

• Develop and evaluate statistical methods for microbiome association analysis that explicitly use ordinal phenotype structure.
• Address challenges like sparsity and zero-inflation in microbiome data.
• Enhance the detection of microbial associations with ordered phenotypes.

Main Methods:

• Proposed three novel methods: binary optimal test, linear trend test, and proportional odds model-based permutation test (POMp).
• Methods account for ordinal phenotype structure and use permutation-based inference for sparsity and zero-inflation.
• Applied methods to three gut microbiome datasets (obesity and colorectal cancer).

Main Results:

• All proposed methods identified differentially abundant features (DAFs) with stronger ordinal associations than existing methods.
• The proportional odds model-based permutation test (POMp) demonstrated superior performance in correlating with phenotype order.
• Identified potential biologically relevant microbial markers using the novel approaches.

Conclusions:

• Incorporating ordinal information is crucial for advancing microbiome studies.
• The developed statistical tools offer robust methods for microbial biomarker discovery in complex diseases.
• Findings support the use of POMp for identifying clinically relevant microbiome signatures.