RT Journal Article
SR Electronic
T1 Statistical Considerations in the Design and Analysis of Longitudinal Microbiome Studies
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 448332
DO 10.1101/448332
A1 Justin D Silverman
A1 Liat Shenhav
A1 Eran Halperin
A1 Sayan Mukherjee
A1 Lawrence A David
YR 2018
UL http://biorxiv.org/content/early/2018/10/20/448332.abstract
AB Longitudinal studies of microbial communities have emphasized that host-associated microbiota are highly dynamic as well as underscoring the potential biomedical relevance of understanding these dynamics. Despite this increasing appreciation, statistical challenges in the design and analysis of longitudinal microbiome studies such as sequence counting, technical variation, signal aliasing, contamination, sparsity, missing data, and algorithmic scalability remain. In this review we discuss these challenges and highlight current progress in the field. Where possible, we try to provide guidelines for best practices as well as discuss how to tailor design and analysis to the hypothesis and ecosystem under study. Overall, this review is intended to serve as an introduction to longitudinal microbiome studies for both statisticians new to the microbiome field as well as biologists with little prior experience with longitudinal study design and analysis.