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comethyl's Introduction

comethyl

An R package for weighted region comethylation network analysis.

Comethyl builds upon the WGCNA package to identify and interpret modules of comethylated regions from whole-genome bisulfite sequencing data. Regions are defined from clusters of CpG sites or from genomic annotations, and then percent methylation values are used to identify comethylation modules.

Interesting modules are identified and explored by comparing with sample traits and examining functional enrichments. Results are then visualized with high-quality, editable plots from ggplot2.

Installation

You can install Comethyl from this repository and load it into your R session with the code below.

install.packages(c("BiocManager", "remotes"))
BiocManager::install("cemordaunt/comethyl")
library(comethyl)

Documentation

Complete documentation for comethyl is available at https://cemordaunt.github.io/comethyl/.

Workflow

Comethyl Workflow

Citing Comethyl

If you use Comethyl in your work, please cite our Briefings in Bioinformatics publication:

Mordaunt CE, Mouat JS, Schmidt RJ, and LaSalle JM. (2022) Comethyl: a network-based methylome approach to investigate the multivariate nature of health and disease. Briefings in Bioinformatics bbab554.

Acknowledgements

Many thanks to Julia Mouat for creating the vignettes, and to both Ben Laufer and Janine LaSalle for very helpful discussions.

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comethyl's Issues

Installation issue

Hi,

I attempted to install comethyl with the instructions given.

export R_LIBS=~/Rlibs
R

version 4.2.1 (2022-06-23) -- "Funny-Looking Kid"
Copyright (C) 2022 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details.

Natural language support but running in an English locale

R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R.

> install.packages(c("BiocManager", "remotes"))

BiocManager::install("cemordaunt/comethyl")

ERROR: dependency ‘genefilter’ is not available for package ‘sva
ERROR: dependencies ‘GenomicFeatures’, ‘AnnotationDbi’ are not available for package ‘TxDb.Hsapiens.UCSC.hg38.knownGene’
ERROR: dependencies ‘GenomicFeatures’, ‘AnnotationDbi’ are not available for package ‘TxDb.Hsapiens.UCSC.hg19.knownGene’
ERROR: dependencies ‘AnnotationDbi’, ‘AnnotationHub’, ‘GenomicFeatures’ are not available for package ‘annotatr’
ERROR: dependencies ‘GenomicFeatures’, ‘AnnotationDbi’ are not available for package ‘bumphunter’
ERROR: dependencies ‘GenomicFeatures’, ‘GO.db’, ‘AnnotationDbi’, ‘TxDb.Hsapiens.UCSC.hg19.knownGene’, ‘TxDb.Hsapiens.UCSC.hg38.knownGene’, ‘org.Hs.eg.db’ are not available for package ‘rGREAT’
ERROR: dependencies ‘bumphunter’, ‘annotatr’, ‘AnnotationHub’ are not available for package ‘dmrseq’

ERROR: dependencies ‘annotatr’, ‘biomaRt’, ‘dmrseq’, ‘GenomicFeatures’, ‘rGREAT’, ‘sva’, ‘WGCNA’ are not available for package ‘comethyl’

I edited this a bit, but it appears there are some missing dependencies that R doesn't know about where to install them from?

I tried using the install instructions for genefilter:

https://bioconductor.org/packages/release/bioc/html/genefilter.html

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

`ERROR: dependency ‘png’ is not available for package ‘KEGGREST’`

I was wondering if you were aware of these issues, or possible had any suggestions on resolving this? The host OS is rhel 7.7 (its a cluster head node.)

I was thinking I could try installing the various dependencies via spack, but there are an awful lot of them... anyhew. What OS is this working on? ubuntu? mac? Did I miss something obvious?

Examples

Hi Charles,

I was hoping to use comethyl on single cell WGBS methylome datasets (pseudo-bulked). Would that be a reasonable direction?

If so, can you please provide me example input files [an example of the Bismark Cytosine Reports and an example of sample_info.xlsx]?

We use an in-house pipeline to map the data and generate signal / count files called allC files which we describe here - https://hq-1.gitbook.io/mc/tech-background/file-formats#allc-file

Thanks,
Manoj.

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