MODIMA, a Method for Multivariate Omnibus Distance Mediation Analysis, Allows for Integration of Multivariate Exposure–Mediator–Response Relationships

Hamidi, Bashir; Wallace, Kristin; Alekseyenko, Alexander V.

doi:10.3390/genes10070524

Cited by 15 publications

(11 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we have determined the testing subset S using statistical feature selection methods. Specifically, motivated by a previous publication ( 32 ) in mediation analysis involving high-dimensional microbial features, we have utilized the statistical framework of distance correlation (DC) learning ( 26 , 33 ) to determine S in the multivariate microbiome association analysis considered here.…”

Section: Methodsmentioning

confidence: 99%

Adaptive and powerful microbiome multivariate association analysis via feature selection

Banerjee¹,

Chen²,

Zhan³

2022

NAR Genomics and Bioinformatics

View full text Add to dashboard Cite

The important role of human microbiome is being increasingly recognized in health and disease conditions. Since microbiome data is typically high dimensional, one popular mode of statistical association analysis for microbiome data is to pool individual microbial features into a group, and then conduct group-based multivariate association analysis. A corresponding challenge within this approach is to achieve adequate power to detect an association signal between a group of microbial features and the outcome of interest across a wide range of scenarios. Recognizing some existing methods’ susceptibility to the adverse effects of noise accumulation, we introduce the Adaptive Microbiome Association Test (AMAT), a novel and powerful tool for multivariate microbiome association analysis, which unifies both blessings of feature selection in high-dimensional inference and robustness of adaptive statistical association testing. AMAT first alleviates the burden of noise accumulation via distance correlation learning, and then conducts a data-adaptive association test under the flexible generalized linear model framework. Extensive simulation studies and real data applications demonstrate that AMAT is highly robust and often more powerful than several existing methods, while preserving the correct type I error rate. A free implementation of AMAT in R computing environment is available at https://github.com/kzb193/AMAT.

show abstract

Section: Methodsmentioning

confidence: 99%

Adaptive and powerful microbiome multivariate association analysis via feature selection

Banerjee¹,

Chen²,

Zhan³

2022

NAR Genomics and Bioinformatics

View full text Add to dashboard Cite

show abstract

“…Because it is challenging to conduct mediation analysis in the high-dimensional setting, some existing methods are limited to testing the overall mediation effect at the community level, while others attemp to identify mediating taxa but have no control of the FDR. Specifically, MedTest [9] and MODIMA [10] base their tests on distance matrices that summarize the high-dimensional data into between-sample dissimilarity measurements, and thus produce a global p-value only. Also, they treat the taxa as if they are all under the same type of null in their permutation procedures that provide the null distributions of the test statistics.…”

Section: Introductionmentioning

confidence: 99%

A New Approach to Testing Mediation of the Microbiome using the LDM

Yue

2021

Preprint

View full text Add to dashboard Cite

BackgroundUnderstanding whether and which microbes played a mediating role between an exposure and a disease outcome are essential for researchers to develop clinical interventions to treat the disease by modulating the microbes. Existing methods for mediation analysis of the microbiome are often limited to a global test of community-level mediation or selection of mediating microbes without control of the false discovery rate (FDR). Further, while the null hypothesis of no mediation at each microbe is a composite null that consists of three types of null (no exposure-microbe association, no microbe-outcome association given the exposure, or neither), most existing methods for the global test such as MedTest and MODIMA treat the microbes as if they are all under the same type of null.MethodsWe propose a new approach based on inverse regression that regresses the (possibly transformed) relative abundance of each taxon on the exposure and the exposure-adjusted outcome to assess the exposure-taxon and taxon-outcome associations simultaneously. Then the association p-values are used to test mediation at both the community and individual taxon levels. This approach fits nicely into our Linear Decomposition Model (LDM) frame-work, so our new method is implemented in the LDM and enjoys all the features of the LDM, i.e., allowing an arbitrary number of taxa to be tested, supporting continuous, discrete, or multivariate exposures and outcomes as well as adjustment of confounding covariates, accom-modating clustered data, and offering analysis at the relative abundance or presence-absence scale. We refer to this new method as LDM-med.ResultsUsing extensive simulations, we showed that LDM-med always controlled the type I error of the global test and had compelling power over existing methods; LDM-med always preserved the FDR of testing individual taxa and had much better sensitivity than alternative approaches. In contrast, MedTest and MODIMA had severely inflated type I error when different taxa were under different types of null. The flexibility of LDM-med for a variety of mediation analyses is illustrated by the application to a murine microbiome dataset.Availability and ImplementationOur new method has been added to our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM.

show abstract

“…The compositional and high-dimensional nature of the microbiome mediators poses a great challenge to standard mediation analyses. Distance-based mediation tests MedTest [Zhang et al, 2018] and MODIMA [Hamidi et al, 2019] have been developed for testing the mediation effect of the entire microbial community. These global tests can achieve good power when many microbial taxa in the community mediate the treatment effect on the outcome.…”

Section: Introductionmentioning

confidence: 99%

A Phylogeny-based Test of Mediation Effect in Microbiome

Hong¹,

Chen²,

Tang³

2021

Preprint

View full text Add to dashboard Cite

Recent studies suggest that the microbiome can be an important mediator in the effect of a treatment on an outcome. Microbiome data generated from sequencing experiments contain the relative abundance of a large number of microbial taxa with their evolutionary relationships represented by a phylogenetic tree. The compositional and high-dimensional nature of the microbiome mediator invalidates standard mediation analyses. We propose a phylogeny-based mediation analysis method (PhyloMed) for the microbiome mediator. PhyloMed models the microbiome mediation effect through a cascade of independent local mediation models on the internal nodes of the phylogenetic tree. Each local model captures the mediation effect of a subcomposition at a given taxonomic resolution. The method improves the power of the mediation test by enriching weak and sparse signals across mediating taxa that tend to cluster on the tree. In each local model, we further empower PhyloMed by using a mixture distribution to obtain the subcomposition mediation test p-value, which takes into account the composite nature of the null hypothesis. PhyloMed enables us to test the overall mediation effect of the entire microbial community and pinpoint internal nodes with significant subcomposition mediation effects. Our extensive simulations demonstrate the validity of PhyloMed and its substantial power gain over existing methods. An application to a real study further showcases the advantages of our method.

show abstract

MODIMA, a Method for Multivariate Omnibus Distance Mediation Analysis, Allows for Integration of Multivariate Exposure–Mediator–Response Relationships

Cited by 15 publications

References 43 publications

Adaptive and powerful microbiome multivariate association analysis via feature selection

Adaptive and powerful microbiome multivariate association analysis via feature selection

A New Approach to Testing Mediation of the Microbiome using the LDM

A Phylogeny-based Test of Mediation Effect in Microbiome

Contact Info

Product

Resources

About