metamicrobiomeR (version 1.2)
Microbiome Data Analysis & Meta-Analysis with GAMLSS-BEZI &
Random Effects
Description
Generalized Additive Model for Location, Scale and Shape (GAMLSS)
with zero inflated beta (BEZI) family for analysis of microbiome relative abundance data
(with various options for data transformation/normalization to address compositional effects) and
random effects meta-analysis models for meta-analysis pooling estimates across microbiome studies
are implemented.
Random Forest model to predict microbiome age based on relative abundances of
shared bacterial genera with the Bangladesh data (Subramanian et al 2014),
comparison of multiple diversity indexes using linear/linear mixed effect models
and some data display/visualization are also implemented.
The reference paper is published by
Ho NT, Li F, Wang S, Kuhn L (2019) .