ramwas: Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms

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) <doi:10.1093/bioinformatics/bty069>.

Package details

AuthorAndrey A Shabalin [aut, cre] (<https://orcid.org/0000-0003-0309-6821>), Shaunna L Clark [aut], Mohammad W Hattab [aut], Karolina A Aberg [aut], Edwin J C G van den Oord [aut]
Bioconductor views BatchEffect Coverage DNAMethylation DifferentialMethylation Normalization Preprocessing PrincipalComponent QualityControl Sequencing Visualization
MaintainerAndrey A Shabalin <andrey.shabalin@gmail.com>
URL https://bioconductor.org/packages/ramwas/
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
if (!requireNamespace("BiocManager", quietly = TRUE))


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ramwas documentation built on Nov. 8, 2020, 8:24 p.m.