microHIMA {HIMA} | R Documentation |
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, FDRcut = 0.05, scale = TRUE, verbose = FALSE)
Arguments
X |
a vector of exposure. |
Y |
a vector of outcome. |
OTU |
a |
COV |
a |
FDRcut |
FDR cutoff applied to define and select significant mediators. Default = |
scale |
logical. Should the function scale the data? Default = |
verbose |
logical. Should the function be verbose? Default = |
Value
A data.frame containing mediation testing results of selected mediators (FDR < FDRcut
).
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.
FDR: false discovery rate of selected significant mediator.
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
## Not run:
# Note: In the following example, M1, M2, and M3 are true mediators.
data(himaDat)
head(himaDat$Example4$PhenoData)
microHIMA.fit <- microHIMA(X = himaDat$Example4$PhenoData$Treatment,
Y = himaDat$Example4$PhenoData$Outcome,
OTU = himaDat$Example4$Mediator,
COV = himaDat$Example4$PhenoData[, c("Sex", "Age")],
scale = FALSE)
microHIMA.fit
## End(Not run)