Getting Started with methyldeconv

Welcome to methyldeconv! This vignette will help you get up and running with cell-type deconvolution for DNA methylation data using the package’s unified interface and included methods.

Installation

You can install methyldeconv from GitHub using the pak package manager:

# install the `pak` package manager
install.packages("pak")

pak::pkg_install("omnideconv/methyldeconv")

Example Usage

methyldeconv can be applied directly to a methylSet from the minfi package, or you can apply each method separately on a beta matrix with Illumina CpG IDs.

Below, we demonstrate how to use the EpiDISH method with example data from minfi:

library(methyldeconv)
library(minfi)
library(minfiData)

# use example data from Minfi
methyl_set <- minfiData::MsetEx
ratio_set <- minfi::ratioConvert(methyl_set)
beta_matrix <- minfi::getBeta(ratio_set)

# run EpiDISH for deconvolution of example data
result <- methyldeconv::deconvolute(methyl_set = methyl_set, method = 'epidish')

Viewing the Results

The result of the deconvolution is a table with the estimated cell-type fractions for each sample. You can view it directly as a nicely formatted table:

knitr::kable(head(result), caption = "Estimated cell-type fractions for the first few samples")

Estimated cell-type fractions for the first few samples
	B	CD4T	Eosino	Mono	NK
5723646052_R02C02	0.1868416	0.1993816	0.1853980	0.3058855	0.1224934
5723646052_R04C01	0.1841371	0.1411284	0.2169230	0.3220845	0.1357269
5723646052_R05C02	0.0000000	0.2990338	0.1252629	0.3593454	0.2163580
5723646053_R04C02	0.0610074	0.1500589	0.2699428	0.4363476	0.0826434
5723646053_R05C02	0.1221793	0.1659357	0.2266461	0.3354384	0.1498004
5723646053_R06C02	0.0000000	0.1364639	0.2388252	0.4611588	0.1635522

For more details, see the package documentation and other vignettes!