HIMA (version 2.2.0)

hima2: Advanced High-dimensional Mediation Analysis

Description

hima2 is an upgraded version of hima for estimating and testing high-dimensional mediation effects.

Usage

hima2(
  formula,
  data.pheno,
  data.M,
  outcome.family = c("gaussian", "binomial", "survival"),
  mediator.family = c("gaussian", "negbin", "compositional"),
  penalty = c("DBlasso", "MCP", "SCAD", "lasso"),
  topN = NULL,
  scale = TRUE,
  verbose = FALSE
)

Value

A data.frame containing mediation testing results of selected mediators.

Arguments

formula

an object of class formula: a symbolic description of the overall effect model, i.e., outcome ~ exposure + covariates, to be fitted. Make sure the "exposure" is the variable of interest, which must be listed as the first variable in the right hand side of the formula. independent variable in the formula. The same covariates will be used in screening and penalized regression.

data.pheno

a data frame containing all the variables listed in the right hand side of the formula. hima2 will scale data.pheno.

data.M

a data.frame or matrix of high-dimensional mediators. Rows represent samples, columns represent variables. hima2 will scale data.M.

outcome.family

either 'gaussian' (default, for normally distributed continuous outcome), 'binomial' (for binay outcome), or 'survival' (for time-to-event outcome), depending on the data type of outcome.

mediator.family

either 'gaussian' (default, for continuous mediators), 'negbin' (i.e., negative binomial, for RNA-seq data as mediators), or 'compositional' (for microbiome data as mediators), depending on the data type of high-dimensional mediators (data.M).

penalty

the penalty to be applied to the model. Either 'DBlasso' (De-biased LASSO, default), 'MCP', 'SCAD', or 'lasso'.

topN

an integer specifying the number of top markers from sure independent screening. Default = NULL. If NULL, topN will be either ceiling(n/log(n)) if outcome.family = 'gaussian', or ceiling(n/(2*log(n))) if outcome.family = 'binomial', 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.

verbose

logical. Should the function be verbose and shows the progression? Default = FALSE.

References

Zhang H, Zheng Y, Zhang Z, Gao T, Joyce B, Yoon G, Zhang W, Schwartz J, Just A, Colicino E, Vokonas P, Zhao L, Lv J, Baccarelli A, Hou L, Liu L. Estimating and Testing High-dimensional Mediation Effects in Epigenetic Studies. Bioinformatics. 2016. DOI: 10.1093/bioinformatics/btw351. PMID: 27357171. PMCID: PMC5048064

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

Zhang H, Zheng Y, Hou L, Zheng C, Liu L. Mediation Analysis for Survival Data with High-Dimensional Mediators. Bioinformatics. 2021. DOI: 10.1093/bioinformatics/btab564. PMID: 34343267. PMCID: PMC8570823

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) {
# Example 1 (continous outcome): 
data(Example1)
head(Example1$PhenoData)

e1 <- hima2(Outcome ~ Treatment + Sex + Age, 
      data.pheno = Example1$PhenoData, 
      data.M = Example1$Mediator,
      outcome.family = "gaussian",
      mediator.family = "gaussian",
      penalty = "MCP",
      scale = FALSE)
e1
attributes(e1)$variable.labels

# Example 2 (binary outcome): 
data(Example2)
head(Example2$PhenoData)

e2 <- hima2(Disease ~ Treatment + Sex + Age, 
      data.pheno = Example2$PhenoData, 
      data.M = Example2$Mediator,
      outcome.family = "binomial",
      mediator.family = "gaussian",
      penalty = "MCP",
      scale = FALSE)
e2
attributes(e2)$variable.labels

# Example 3 (time-to-event outcome): 
data(Example3)
head(Example3$PhenoData)

e3 <- hima2(Surv(Status, Time) ~ Treatment + Sex + Age, 
      data.pheno = Example3$PhenoData, 
      data.M = Example3$Mediator,
      outcome.family = "survival",
      mediator.family = "gaussian",
      penalty = "DBlasso",
      scale = FALSE)
e3
attributes(e3)$variable.labels

# Example 4 (compositional data as mediator, e.g., microbiome): 
data(Example4)
head(Example4$PhenoData)

e4 <- hima2(Outcome ~ Treatment + Sex + Age, 
      data.pheno = Example4$PhenoData, 
      data.M = Example4$Mediator,
      outcome.family = "gaussian",
      mediator.family = "compositional",
      penalty = "DBlasso",
      scale = FALSE)
e4
attributes(e4)$variable.labels
}