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] (<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|
|Maintainer||Andrey A Shabalin <[email protected]>|
|Package repository||View on Bioconductor|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.