Yi-Juan Hu1, Glen A Satten2. 1. Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322. 2. Centers for Disease Control and Prevention, Atlanta, GA 30333, USA.
Abstract
MOTIVATION: Methods for analyzing microbiome data generally fall into one of two groups: tests of the global hypothesis of any microbiome effect, which do not provide any information on the contribution of individual operational taxonomic units (OTUs); and tests for individual OTUs, which 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. Further, many tests of individual OTU effects do not preserve the false discovery rate (FDR). RESULTS: 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 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 sample correlation. 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. For global testing, our simulations indicate the LDM provided correct type I error and can have 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. AVAILABILITY AND IMPLEMENTATION: The R package LDM is available on GitHub at https://github.com/yijuanhu/LDM in formats appropriate for Macintosh or Windows. CONTACT: yijuan.hu@emory.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Methods for analyzing microbiome data generally fall into one of two groups: tests of the global hypothesis of any microbiome effect, which do not provide any information on the contribution of individual operational taxonomic units (OTUs); and tests for individual OTUs, which 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. Further, many tests of individual OTU effects do not preserve the false discovery rate (FDR). RESULTS: 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 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 sample correlation. 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. For global testing, our simulations indicate the LDM provided correct type I error and can have 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. AVAILABILITY AND IMPLEMENTATION: The R package LDM is available on GitHub at https://github.com/yijuanhu/LDM in formats appropriate for Macintosh or Windows. CONTACT: yijuan.hu@emory.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Ann E Rogers; Yi-Juan Hu; Ye Yue; Emily F Wissel; Robert A Petit Iii; Simone Jarrett; Jennifer Christie; Timothy D Read Journal: PeerJ Date: 2021-05-11 Impact factor: 2.984
Authors: Anne L Dunlop; Glen A Satten; Yi-Juan Hu; Anna K Knight; Cherie C Hill; Michelle L Wright; Alicia K Smith; Timothy D Read; Bradley D Pearce; Elizabeth J Corwin Journal: Front Cell Infect Microbiol Date: 2021-04-29 Impact factor: 5.293