TY - JOUR T1 - Testing hypotheses about the microbiome using the linear decomposition model JF - bioRxiv DO - 10.1101/229831 SP - 229831 AU - Yi-Juan Hu AU - Glen A. Satten Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/01/29/229831.abstract N2 - Background Distance-based methods for analyzing microbiome data are typically restricted to testing the global hypothesis of microbiome effect, but do not test the contribution of individual operational taxonomic units (OTUs). 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.Methods We introduce 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), and a connection to distance-based ordination. The LDM accommodates both continuous and discrete variables (e.g., clinical outcomes, environmental factors) as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based p-values that can control for correlation (e.g., repeated measurements on the same individual). The LDM can also be applied to transformed data, and an “omnibus” test can easily combine results from analyses conducted on different transformation scales. We also provide a new implementation of PERMANOVA based on our approach.Results For global testing, our simulations indicate the LDM provided correct type I error, even with substantial confounding and/or correlations, and can has comparable power to existing distance-based methods. For testing individual OTUs, our simulations indicate the LDM controlled the FDR well. In contrast, DESeq2 often had inflated FDR; MetagenomeSeq generally had the lowest sensitivity. The flexibility of the LDM for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. We also show that our implementation of PERMANOVA can outperform existing implementations.Conclusions The LDM is a powerful method for global and OTU-specific testing with a natural connection between the two. The LDM is also capable of handling the confounders and correlated data that frequently occur in modern microbiome studies. ER -