Description Usage Arguments Value Examples
The function takes a list of clusters (given as vector of probe names), and perform a GEE analysis of the beta values corresponding to these clusters. i.e. for each cluster of probes a GEE model is fit, were the sites in the clusters are treated as multiple outcomes. The analysis assumes a common exposure effect on all outcomes, but different intercept for each sites.
1 | GEE.clusters(betas, clusters.list, exposure, covariates, id, working.cor = "ex", result.file.name = NULL)
|
betas |
An (m by n) matrix of methylation values of n participants measured on m methylation sites. |
clusters.list |
A list where each item is a vector of methylation sites, composing a single cluster. |
exposure |
The exposure of interest, a vector ordered according to the columns of the matrix betas. |
covariates |
An (n by p) matrix of covariates. The order of rows should match the order of columns of the matrix betas. |
id |
A vector of ids of $n$ participants. The order should match the order of columns of the matrix betas. |
working.cor |
Assume working correlation structure. Could be “unstructured" (not recommended) “independence", “exchangeable", or “ar1". |
result.file.name |
Name of file to print analysis results to. |
A data frame where each row provides a vector with probes in the cluster, the estimated effect size of exposure on the cluster, estimated (sandwich) standard error, p-value, the number of sites in the cluster and the chromosome in which the cluster is.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | data(betas.7) ## upload methylation data
exposure <- rbinom(ncol(betas.7), 1,prob = 0.5) ## generate random exposure
covariates <- matrix(rnorm(2*ncol(betas.7)), ncol = 2)
rownames(covariates) <- colnames(betas.7)
### not run! to use package from Bioconductor to create a new annotation object
# source("http://bioconductor.org/biocLite.R")
# biocLite("IlluminaHumanMethylation450k.db")
# require(IlluminaHumanMethylation450k.db)
# annot.7 <- create.annot.triche(rownames(betas.7))
data(annot.7)
clusters.list <- assign.to.clusters(betas.7, annot.7)
GEE.results.clusters <- GEE.clusters(betas.7, clusters.list, exposure, covariates, id = colnames(betas.7), working.cor = "ex")
top.clusters.summary <- summarize.top.clusters(betas.7, covariates, exposure, id = colnames(betas.7), GEE.results.clusters, "results.tex", annot= annot.7)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.