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High-dimensional mediation analysis for compositional microbiome data

Source: R/hima_microbiome.R
hima_microbiome.Rd

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

Usage

hima_microbiome(X, OTU, Y, COV = NULL, FDRcut = 0.05, verbose = FALSE)

Arguments

X

a vector of exposure. Do not use data.frame or matrix.

OTU

a data.frame or matrix of high-dimensional Operational Taxonomic Unit (OTU) data (mediators). Rows represent samples, columns represent variables.

Y

a vector of continuous outcome. Binary outcome is not allowed. Do not use data.frame or matrix.

COV

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

FDRcut

Hommel FDR cutoff applied to select significant mediators. Default = 0.05.

verbose

logical. Should the function be verbose? Default = FALSE.

Value

A data.frame containing mediation testing results of significant mediators (FDR <FDRcut).

Index:

mediation name of selected significant mediator.

alpha_hat:

coefficient estimates of exposure (X) –> mediators (M) (adjusted for covariates).

alpha_se:

standard error for alpha.

beta_hat:

coefficient estimates of mediators (M) –> outcome (Y) (adjusted for covariates and exposure).

beta_se:

standard error for beta.

IDE:

mediation (indirect) effect, i.e., alpha*beta.

rimp:

relative importance of the mediator.

pmax:

joint raw p-value of selected significant mediator (based on Hommel FDR method).

References

1. 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

2. 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) { # \dontrun{
# Note: In the following example, M1, M2, and M3 are true mediators.

head(MicrobiomeData$PhenoData)

hima_microbiome.fit <- hima_microbiome(
  X = MicrobiomeData$PhenoData$Treatment,
  Y = MicrobiomeData$PhenoData$Outcome,
  OTU = MicrobiomeData$Mediator,
  COV = MicrobiomeData$PhenoData[, c("Sex", "Age")],
  FDRcut = 0.05,
  verbose = TRUE
)
hima_microbiome.fit
} # }