| Title: | A Causal Mediation Method with Methylated Region (MR) as the Mediator |
|---|---|
| Description: | A causal mediation approach under the counterfactual framework to test the significance of total, direct and indirect effects. In this approach, a group of methylated sites from a predefined region are utilized as the mediator, and the functional transformation is used to reduce the possible high dimension in the region-based methylated sites and account for their location information. |
| Authors: | Qi Yan |
| Maintainer: | Qi Yan <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.1 |
| Built: | 2024-11-20 06:36:30 UTC |
| Source: | CRAN |
data. phenotype file. 1st column is ID, 2nd column is continuous outcome, 3rd column is binary outcome, 4th column is exposure, 5th column is age, 6th column is gender, 7th-last columns are CpGs
pos. CpG locations from the defined region and they are from the same chromosome.
data(example_data)data(example_data)
A causal mediation method with methylated region as the mediator
mediation( pheno, predictor, region, pos, order, gbasis, covariate, base = "bspline", family = "gaussian" )mediation( pheno, predictor, region, pos, order, gbasis, covariate, base = "bspline", family = "gaussian" )
pheno |
A vector of continuous or binary phenotypes (class: numeric). |
predictor |
A vector of values for the exposure variable (class: numeric). |
region |
A matrix of CpGs in a region. Each column is a CpG (class: data.frame). |
pos |
A vector of CpG locations from the defined region and they are from the same chromosome (class: integer). |
order |
A value for the order of bspline basis. 1: constant, 2: linear, 3: quadratic and 4: cubic. |
gbasis |
A value for the number of basis being used for functional transformation on CpGs. |
covariate |
A matrix of covariates. Each column is a covariate (class: data.frame). |
base |
"bspline" for B-spline basis or "fspline" for Fourier basis. |
family |
"gaussian" for continuous outcome or "binomial" for binary outcome. |
1. pval$TE: total effect (TE) p-value
2. pval$DE: direct effect (DE) p-value
3. pval$IE: indirect effect (IE) p-value
4. pval_MX: p-value for the association between methylation and exposure
################ ### Examples ### ################ data("example_data") predictor = data$exposure region = data[,7:dim(data)[2]] covariates = subset(data, select=c("age","gender")) # binary outcome pheno_bin = data$pheno_bin mediation(pheno_bin, predictor, region, pos, covariate=covariates, order=4, gbasis=4, base="bspline", family="binomial") # continuous outcome pheno_con = data$pheno_con mediation(pheno_con, predictor, region, pos, covariate=covariates, order=4, gbasis=4, base="bspline", family="gaussian")################ ### Examples ### ################ data("example_data") predictor = data$exposure region = data[,7:dim(data)[2]] covariates = subset(data, select=c("age","gender")) # binary outcome pheno_bin = data$pheno_bin mediation(pheno_bin, predictor, region, pos, covariate=covariates, order=4, gbasis=4, base="bspline", family="binomial") # continuous outcome pheno_con = data$pheno_con mediation(pheno_con, predictor, region, pos, covariate=covariates, order=4, gbasis=4, base="bspline", family="gaussian")
A causal mediation method with a single CpG site as the mediator
mediation_single(pheno, predictor, cpg, covariate, family = "gaussian")mediation_single(pheno, predictor, cpg, covariate, family = "gaussian")
pheno |
A vector of continuous or binary phenotypes (class: numeric). |
predictor |
A vector of values for the exposure variable (class: numeric). |
cpg |
A vector of a CpG (class: numeric). |
covariate |
A matrix of covariates. Each column is a covariate (class: data.frame). |
family |
"gaussian" for continuous outcome or "binomial" for binary outcome. |
1. pval$TE: total effect (TE) p-value
2. pval$DE: direct effect (DE) p-value
3. pval$IE: indirect effect (IE) p-value
4. pval_MX: p-value for the association between methylation and exposure
################ ### Examples ### ################ data("example_data") predictor = data$exposure cpg = data[,9] #any number in c(7:dim(data)[2]) covariates = subset(data, select=c("age","gender")) # binary outcome pheno_bin = data$pheno_bin mediation_single(pheno_bin, predictor, cpg, covariate=covariates, family="binomial") # continuous outcome pheno_con = data$pheno_con mediation_single(pheno_con, predictor, cpg, covariate=covariates, family="gaussian")################ ### Examples ### ################ data("example_data") predictor = data$exposure cpg = data[,9] #any number in c(7:dim(data)[2]) covariates = subset(data, select=c("age","gender")) # binary outcome pheno_bin = data$pheno_bin mediation_single(pheno_bin, predictor, cpg, covariate=covariates, family="binomial") # continuous outcome pheno_con = data$pheno_con mediation_single(pheno_con, predictor, cpg, covariate=covariates, family="gaussian")