ramwas

DOI: 10.18129/B9.bioc.ramwas    

Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms

Bioconductor version: Release (3.6)

RaMWAS provides a complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net. Additionally, RaMWAS include tools for joint analysis of methlyation and genotype data.

Author: Andrey A Shabalin [aut, cre], Shaunna L Clark [aut], Mohammad W Hattab [aut], Karolina A Aberg [aut], Edwin J C G van den Oord [aut]

Maintainer: Andrey A Shabalin <ashabalin at vcu.edu>

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("ramwas")

Documentation

HTML	R Script	1. Overview
HTML	R Script	2. CpG sets
HTML	R Script	3. BAM Quality Control Measures
HTML	R Script	4. Joint Analysis of Methylation and Genotype Data
HTML	R Script	5. Analyzing data from other sources
HTML	R Script	6. RaMWAS parameters
PDF		Reference Manual
Text		NEWS

Details

biocViews	BatchEffect, Coverage, DNAMethylation, DifferentialMethylation, Normalization, Preprocessing, PrincipalComponent, QualityControl, Sequencing, Software, Visualization
Version	1.2.0
In Bioconductor since	BioC 3.5 (R-3.4) (1 year)
License	LGPL-3
Depends	R (>= 3.3.0), methods, filematrix
Imports	graphics, stats, utils, digest, glmnet, KernSmooth, grDevices, GenomicAlignments, Rsamtools, parallel, biomaRt, Biostrings, BiocGenerics
LinkingTo
Suggests	knitr, rmarkdown, pander, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, SNPlocs.Hsapiens.dbSNP144.GRCh37, BSgenome.Ecoli.NCBI.20080805
SystemRequirements
Enhances
URL	https://bioconductor.org/packages/ramwas/
BugReports	https://github.com/andreyshabalin/ramwas/issues
Depends On Me
Imports Me
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Build Report

Package Archives

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

Source Package	ramwas_1.2.0.tar.gz
Windows Binary	ramwas_1.2.0.zip (32- & 64-bit)
Mac OS X 10.11 (El Capitan)	ramwas_1.2.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/ramwas
Package Short Url	http://bioconductor.org/packages/ramwas/
Package Downloads Report	Download Stats