Rob Knight

TL;DR: In this paper , the authors show that sample collection time affects the conclusions drawn from microbiome studies and are larger than the effect size of a daily experimental intervention or dietary changes, and that the timing of divergence of the microbiome composition between experimental and control groups are unique to each experiment.

TL;DR: It is found that the urinary microbiome is distinct from nearby sites and is unrelated to stool microbiome, and age, menopausal status, prior UTI and host genetics were top among factors defining the urobiome.

TL;DR: Evident, a package for effect size calculations and power analysis on microbiome data and show that Evident scales to large datasets with numerous metadata covariates.

TL;DR: In this paper, the authors proposed a new approach to model Beta-diversity as a response within a regression setting by utilizing the functional response models (FRM), a class of semiparametric models for between- as well as within-subject attributes.