Document Type

Article

Publication Date

12-27-2019

Keywords

JGM

JAX Source

Sci Rep 2019 Dec 27; 9(1):20081

Volume

9

Issue

1

First Page

20082

Last Page

20082

ISSN

2045-2322

PMID

31882682

DOI

https://doi.org/10.1038/s41598-019-56397-9

Abstract

Regressing an outcome or dependent variable onto a set of input or independent variables allows the analyst to measure associations between the two so that changes in the outcome can be described by and predicted by changes in the inputs. While there are many ways of doing this in classical statistics, where the dependent variable has certain properties (e.g., a scalar, survival time, count), little progress on regression where the dependent variable are microbiome taxa counts has been made that do not impose extremely strict conditions on the data. In this paper, we propose and apply a new regression model combining the Dirichlet-multinomial distribution with recursive partitioning providing a fully non-parametric regression model. This model, called DM-RPart, is applied to cytokine data and microbiome taxa count data and is applicable to any microbiome taxa count/metadata, is automatically fit, and intuitively interpretable. This is a model which can be applied to any microbiome or other compositional data and software (R package HMP) available through the R CRAN website.

Comments

This open access article is licensed under a Creative Commons Attribution 4.0 International License

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