An analysis of methylation data operates in a few steps. First, clustering of neighboring CpG sites to sets (some clusters will have only a single site). Then, performs GEE analysis of the methylation regions, treated as outcomes, as affected by a single exposure. (The sandwich standard errors are used). The results are reported and optionally printed to a latex table.
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First, load annotation. The proceed create a cluster list using the functions assign.to.clusters. To find association with exposure, the clusters are then analyzed using GEEs by the function GEE.clusters. Finally, top clusters are summarized and printed to latex tables using summarize.top.clusters.
Maintainer: Tamar Sofer <[email protected]>
Sofer, T, Schifano, ED, Hoppin, JA, Hou^*, L and Baccarelli^* AA, 2013. “A-clustering: A Novel Method for the Detection of Co-regulated Methylation Regions, and Regions Associated with Exposure".
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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) data(annot) ## load annotation created using the IlluminaHumanMethylation450k.db package on July 2013 clusters.list <- assign.to.clusters(betas.7, annot) 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)
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