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High-dimensional mediation analysis with de-biased lasso penalty

Source: R/hima_dblasso.R
hima_dblasso.Rd

hima_dblasso is used to estimate and test high-dimensional mediation effects using de-biased lasso penalty.

Usage

hima_dblasso(
  X,
  M,
  Y,
  COV = NULL,
  topN = NULL,
  scale = TRUE,
  FDRcut = 0.05,
  verbose = FALSE,
  parallel = FALSE,
  ncore = 1
)

Arguments

X

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

M

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

Y

a vector of outcome. Can be either continuous or binary (0-1). Do not use data.frame or matrix.

COV

a data.frame or matrix of covariates dataset for testing the association M ~ X and Y ~ M.

topN

an integer specifying the number of top markers from sure independent screening. Default = NULL. If NULL, topN will be ceiling(n/log(n)), where n is the sample size. If the sample size is greater than topN (pre-specified or calculated), all mediators will be included in the test (i.e. low-dimensional scenario).

scale

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

FDRcut

HDMT pointwise FDR cutoff applied to select significant mediators. Default = 0.05.

verbose

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

parallel

logical. Enable parallel computing feature? Default = FALSE.

ncore

number of cores to run parallel computing Valid when parallel = TRUE.

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 HDMT pointwise FDR method).

References

Perera C, Zhang H, Zheng Y, Hou L, Qu A, Zheng C, Xie K, Liu L. HIMA2: high-dimensional mediation analysis and its application in epigenome-wide DNA methylation data. BMC Bioinformatics. 2022. DOI: 10.1186/s12859-022-04748-1. PMID: 35879655; PMCID: PMC9310002

Examples

if (FALSE) { # \dontrun{
# Note: In the following examples, M1, M2, and M3 are true mediators.

# Y is continuous and normally distributed
# Example:
data(ContinuousOutcome)
pheno_data <- ContinuousOutcome$PhenoData
mediator_data <- ContinuousOutcome$Mediator

hima_dblasso.fit <- hima_dblasso(
  X = pheno_data$Treatment,
  Y = pheno_data$Outcome,
  M = mediator_data,
  COV = pheno_data[, c("Sex", "Age")],
  scale = FALSE, # Disabled only for simulation data
  FDRcut = 0.05,
  verbose = TRUE
)
hima_dblasso.fit
} # }