Abstract
Differential abundance analysis is a crucial task in many microbiome studies, where the central goal is to identify microbiome taxa associated with certain biological or clinical conditions. There are two different modes of microbiome differential abundance analysis: the individual-based univariate differential abundance analysis and the group-based multivariate differential abundance analysis. The univariate analysis identifies differentially abundant microbiome taxa subject to multiple correction under certain statistical error measurements such as false discovery rate, which is typically complicated by the high-dimensionality of taxa and complex correlation structure among taxa. The multivariate analysis evaluates the overall shift in the abundance of microbiome composition between two conditions, which provides useful preliminary differential information for the necessity of follow-up validation studies. In this paper, we present a novel Adaptive multivariate two-sample test for Microbiome Differential Analysis (AMDA) to examine whether the composition of a taxa-set are different between two conditions. Our simulation studies and real data applications demonstrated that the AMDA test was often more powerful than several competing methods while preserving the correct type I error rate. A free implementation of our AMDA method in R software is available at https://github.com/xyz5074/AMDA.
Highlights
The human microbiome, referred as the aggregate of microorganisms that resides on or within any human tissues and biofluids, has recently gained substantial scientific interest due to its vital role in many human health and disease conditions, including but are not limited to obesity (Turnbaugh et al, 2009), type 2 diabetes (Qin et al, 2012), rheumatoid arthritis (Zhang et al, 2015), inflammatory bowel disease (Morgan et al, 2015), bacterial vaginosis (Mitchell et al, 2017), and colorectal cancer (Louis et al, 2014)
Besides the limitation in results’ interpretation, it may jeopardize the power of the test when the taxa-set contains many taxa that are not differentially abundant (Cao et al, 2017). To enhance both interpretation and power of existing multivariate analysis in the framework of Microbiome differential abundance analysis (MDA), we propose a two-stage Adaptive Microbiome Differential Analysis (AMDA) procedure, which first selects some putative taxa that are more likely to be differentially abundant between two conditions, and examines the differential abundances of the selected taxa-set with a multivariate two-sample test using Maximum Mean Discrepancy (MMD) (Gretton et al, 2007, 2012)
A comprehensive simulation study has been conducted to compare the performance of AMDA to a wide range of existing microbiome association tests in the framework of microbiome differential abundance analysis
Summary
The human microbiome, referred as the aggregate of microorganisms that resides on or within any human tissues and biofluids, has recently gained substantial scientific interest due to its vital role in many human health and disease conditions, including but are not limited to obesity (Turnbaugh et al, 2009), type 2 diabetes (Qin et al, 2012), rheumatoid arthritis (Zhang et al, 2015), inflammatory bowel disease (Morgan et al, 2015), bacterial vaginosis (Mitchell et al, 2017), and colorectal cancer (Louis et al, 2014). Many studies attempt to detect differentially abundant microbiome features (species/OTUs) between two predefined classes of samples, where a microbiome feature is considered differentially abundant, if its mean proportion is significantly different between two conditions. This type of analysis can improve understanding the pathology of the disease from a microbiome perspective and potentially lead to preventive or therapeutic strategies (Virgin and Todd, 2011). New statistical methods for microbiome differential abundance analysis are desired
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
