ramwas
Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms
Bioconductor version: Release (3.20)
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. This work is published in Bioinformatics, Shabalin et al. (2018)
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 <andrey.shabalin at gmail.com>
citation("ramwas")
):
Installation
To install this package, start R (version "4.4") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ramwas")
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("ramwas")
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.a. Analyzing Illumina Methylation Array Data | HTML | R Script |
5.c. Analyzing data from other sources | HTML | R Script |
6. RaMWAS parameters | HTML | R Script |
Reference Manual | ||
NEWS | Text |
Details
biocViews | BatchEffect, Coverage, DNAMethylation, DifferentialMethylation, Normalization, Preprocessing, PrincipalComponent, QualityControl, Sequencing, Software, Visualization |
Version | 1.30.0 |
In Bioconductor since | BioC 3.5 (R-3.4) (7.5 years) |
License | LGPL-3 |
Depends | R (>= 3.3.0), methods, filematrix |
Imports | graphics, stats, utils, digest, glmnet, KernSmooth, grDevices, GenomicAlignments, Rsamtools, parallel, biomaRt, Biostrings, BiocGenerics |
System Requirements | |
URL | https://bioconductor.org/packages/ramwas/ |
Bug Reports | https://github.com/andreyshabalin/ramwas/issues |
See More
Suggests | knitr, rmarkdown, pander, BiocStyle, BSgenome.Ecoli.NCBI.20080805 |
Linking To | |
Enhances | |
Depends On Me | |
Imports Me | |
Suggests Me | |
Links To Me | |
Build Report | Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
Source Package | ramwas_1.30.0.tar.gz |
Windows Binary (x86_64) | ramwas_1.30.0.zip (64-bit only) |
macOS Binary (x86_64) | ramwas_1.30.0.tgz |
macOS Binary (arm64) | ramwas_1.29.0.tgz |
Source Repository | git clone https://git.bioconductor.org/packages/ramwas |
Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/ramwas |
Bioc Package Browser | https://code.bioconductor.org/browse/ramwas/ |
Package Short Url | https://bioconductor.org/packages/ramwas/ |
Package Downloads Report | Download Stats |