HIMA (version 2.2.0)

microHIMA: High-dimensional mediation analysis for compositional microbiome data

Description

microHIMA is used to estimate and test high-dimensional mediation effects for compositional microbiome data.

Usage

microHIMA(X, Y, OTU, COV = NULL, FDPcut = 0.05, scale = TRUE)

Value

A data.frame containing mediation testing results of selected mediators (FDP < FDPcut).

• ID: index of selected significant mediator.

• alpha: coefficient estimates of exposure (X) --> mediators (M).

• alpha_se: standard error for alpha.

• beta: coefficient estimates of mediators (M) --> outcome (Y) (adjusted for exposure).

• beta_se: standard error for beta

• p_FDP: false discovery proportions of selected significant mediator.

Arguments

X

a vector of exposure.

Y

a vector of outcome.

OTU

a data.frame or matrix of high-dimensional compositional OTUs (mediators). Rows represent samples, columns represent variables.

COV

a data.frame or matrix of adjusting covariates. Rows represent samples, columns represent microbiome variables. Can be NULL.

FDPcut

FDP (false discovery proportions) cutoff applied to define and select significant mediators. Default = 0.05.

scale

logical. Should the function scale the data? Default = TRUE.

References

Zhang H, Chen J, Feng Y, Wang C, Li H, Liu L. Mediation effect selection in high-dimensional and compositional microbiome data. Stat Med. 2021. DOI: 10.1002/sim.8808. PMID: 33205470; PMCID: PMC7855955.

Zhang H, Chen J, Li Z, Liu L. Testing for mediation effect with application to human microbiome data. Stat Biosci. 2021. DOI: 10.1007/s12561-019-09253-3. PMID: 34093887; PMCID: PMC8177450.

Examples

if (FALSE) {
data(Example4)
head(Example4$PhenoData)

microHIMA.fit <- microHIMA(X = Example4$PhenoData$Treatment, 
                           Y = Example4$PhenoData$Outcome, 
                           OTU = Example4$Mediator, 
                           COV = Example4$PhenoData[, c("Sex", "Age")],
                           scale = FALSE)
microHIMA.fit
}