Coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies.

ABSTRACT:

Background

One of the main challenges of microbiome analysis is its compositional nature that if ignored can lead to spurious results. Addressing the compositional structure of microbiome data is particularly critical in longitudinal studies where abundances measured at different times can correspond to different sub-compositions.

Results

We developed coda4microbiome, a new R package for analyzing microbiome data within the Compositional Data Analysis (CoDA) framework in both, cross-sectional and longitudinal studies. The aim of coda4microbiome is prediction, more specifically, the method is designed to identify a model (microbial signature) containing the minimum number of features with the maximum predictive power. The algorithm relies on the analysis of log-ratios between

SUBMITTER: Calle ML 

PROVIDER: S-EPMC9990256 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature