hima_dblasso: High-dimensional mediation analysis with de-biased lasso...

In HIMA: High-Dimensional Mediation Analysis

High-dimensional mediation analysis with de-biased lasso penalty

Description

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
)

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.

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

## Not run: 
# Note: In the following examples, M1, M2, and M3 are true mediators.

# Y is continuous and normally distributed
# Example:
head(ContinuousOutcome$PhenoData)

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

## End(Not run)

HIMA documentation built on April 4, 2025, 3:37 a.m.