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Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

Guided Protocol for Fecal Microbial Characterization by 16S rRNA-Amplicon Sequencing

Published on: March 19, 2018

New statistical method identifies cytokines that distinguish stool microbiomes.

Dake Yang¹, Jethro Johnson², Xin Zhou²

¹BioRankings, St. Louis, MO, USA.

Scientific Reports

|December 29, 2019

Summary

We introduce a new non-parametric regression model, Dirichlet-multinomial distribution with recursive partitioning (DM-RPart), for analyzing microbiome taxa counts. This method offers an interpretable approach for microbiome data analysis without strict data conditions.

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Area of Science:

Microbiology
Statistical Modeling
Bioinformatics

Background:

Regression analysis is crucial for understanding variable associations, but standard methods struggle with complex microbiome taxa count data.
Existing regression models for microbiome data often require restrictive assumptions, limiting their applicability.

Purpose of the Study:

To develop a flexible, non-parametric regression model for microbiome taxa count data.
To provide an interpretable and automatically fitting model for analyzing microbiome composition and associated metadata.

Main Methods:

The proposed model, Dirichlet-multinomial with recursive partitioning (DM-RPart), combines statistical distributions with tree-based partitioning.
This approach allows for non-parametric regression on microbiome taxa counts and other compositional data.

Main Results:

The DM-RPart model was successfully applied to both cytokine and microbiome taxa count data.
The model demonstrates applicability to diverse microbiome datasets and associated metadata.

Conclusions:

DM-RPart offers a novel and effective regression framework for microbiome and compositional data analysis.
The associated R package (HMP) is available on CRAN, facilitating broader adoption and application.