methylationArrayAnalysis

A cross-package Bioconductor workflow for analysing methylation array data.


Bioconductor version: Release (3.19)

Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This Bioconductor workflow uses multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data.

Author: Jovana Maksimovic [aut, cre]

Maintainer: Jovana Maksimovic <jovana.maksimovic at mcri.edu.au>

Citation (from within R, enter citation("methylationArrayAnalysis")):

Installation

To install this package, start R (version "4.4") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("methylationArrayAnalysis")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("methylationArrayAnalysis")
A cross-package Bioconductor workflow for analysing methylation array data HTML R Script

Details

biocViews EpigeneticsWorkflow, Workflow
Version 1.28.0
License Artistic-2.0
Depends R (>= 3.3.0), knitr, rmarkdown, BiocStyle, limma, minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, RColorBrewer, missMethyl, matrixStats, minfiData, Gviz, DMRcate, stringr, FlowSorted.Blood.450k
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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package methylationArrayAnalysis_1.28.0.tar.gz
Windows Binary (x86_64)
macOS Binary (x86_64)
macOS Binary (arm64)
Source Repository git clone https://git.bioconductor.org/packages/methylationArrayAnalysis
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/methylationArrayAnalysis
Package Short Url https://bioconductor.org/packages/methylationArrayAnalysis/
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