May 22, 2019
*Note special time & location*
12:30 - 1:30 pm, Location: B1-072/074, Weintraub Building
Glen Satten, Centers for Disease Control
Analyzing Microbiome Data Using the Linear Decomposition Model
Abstract. Distance-based methods for analyzing microbiome data are typically restricted to testing the global hypothesis of any effect of the microbiome on a trait of interest, but do not test the contribution of individual bacteria (operational taxonomic units OTUs or amplicon sequence variants ASVs). Conversely, tests for individual OTUs do not typically provide a global test of microbiome effect. Without a unified approach, the findings of a global test may be hard to resolve with the findings at the individual OTU level. In addition, many existing methods cannot be applied to complex studies such as those with confounders and correlated data. To bridge this gap, we have proposed the linear decomposition model (LDM) that provides a single analysis path that includes global tests of any effect of the microbiome, tests of the effects of individual OTUs while accounting for multiple testing by controlling the false discovery rate (FDR). The LDM accommodates both continuous and discrete variables and allows for adjustment of confounding covariates. We also show how to analyze matched sets of microbiome data using the LDM, and consider applying the LDM to presence-absence data. If time permits, I will also describe a general approach to testing association between groups of OTUs (e.g., species, genera, families etc.) that have a tree-structured dependence structure using a novel bottom-up approach.