| Type: | Package |
| Title: | A Causal Mediation Method with Methylated Region (MR) as the Mediator |
| Version: | 1.0.1 |
| Author: | Qi Yan |
| Maintainer: | Qi Yan <qy2253@cumc.columbia.edu> |
| 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. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| LazyData: | true |
| Depends: | R (≥ 3.5.0), fda |
| Imports: | MASS, stats |
| RoxygenNote: | 7.1.0 |
| Collate: | 'MRmediation.R' 'mediation_single.R' 'example_data.R' |
| NeedsCompilation: | no |
| Packaged: | 2020-12-17 17:45:14 UTC; qiyan |
| Repository: | CRAN |
| Date/Publication: | 2020-12-17 22:50:16 UTC |
This is the data for examples
Description
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.
Usage
data(example_data)
A causal mediation method with methylated region as the mediator
Description
A causal mediation method with methylated region as the mediator
Usage
mediation(
pheno,
predictor,
region,
pos,
order,
gbasis,
covariate,
base = "bspline",
family = "gaussian"
)
Arguments
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. |
Value
1. pval$TE: total effect (TE) p-value
2. pval$DE: direct effect (DE) p-value
3. pval$IE: indirect effect (IE) p-value
4. pval_MX: p-value for the association between methylation and exposure
Examples
################
### Examples ###
################
data("example_data")
predictor = data$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
Description
A causal mediation method with a single CpG site as the mediator
Usage
mediation_single(pheno, predictor, cpg, covariate, family = "gaussian")
Arguments
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. |
Value
1. pval$TE: total effect (TE) p-value
2. pval$DE: direct effect (DE) p-value
3. pval$IE: indirect effect (IE) p-value
4. pval_MX: p-value for the association between methylation and exposure
Examples
################
### Examples ###
################
data("example_data")
predictor = data$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")