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methylationArrayAnalysis

A cross-package Bioconductor workflow for analysing methylation array data

Bioconductor version: 3.23 · Package version: 1.36.0

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.

Installation

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

BiocManager::install("methylationArrayAnalysis")

Details

MaintainerJovana Maksimovic <jovana.maksimovic@petermac.org>
AuthorJovana Maksimovic [aut, cre]
LicenseArtistic-2.0
Downloads rank136
Source branchRELEASE_3_23
biocViewsEpigeneticsWorkflow, Workflow

Download

Follow the installation instructions to use this package in your R session.

Source packagemethylationArrayAnalysis_1.36.0.tar.gz
Dependencies

Depends: R (>= 3.3.0), knitr, rmarkdown, BiocStyle, limma, minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, RColorBrewer, missMethyl, matrixStats, minfiData, Gviz, DMRcate, stringr, FlowSorted.Blood.450k