Aclust-package: Aclust package implements the A-clustering algorithm and...

In ftyu1234/Aclust: Clustering functions and analysis pipeline for methylation outcome data based on clustering and GEEs

Description

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.

Details

Package:	Aclust
Type:	Package
Version:	2.0.1
Date:	2014-08-18
License:	What license is it under?

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.

Author(s)

Tamar Sofer

Maintainer: Tamar Sofer <[e-mail]>

References

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".

Examples

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.7)  ## load annotation created using the IlluminaHumanMethylation450k.db package on July 2013
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)

ftyu1234/Aclust documentation built on May 16, 2019, 3:37 p.m.