Ever wanted to apply cell-type deconvolution on your DNA methylation data but could not decide which method to use? Here is methylDeconv to help your needs!
This package integrates unified access to five reference-based cell-type deconvolution methods that can directly be applied to Illumina array data (450k, EPIC arrays) or bisulfite sequencing data (RRBS, WGBS).
The included methods are:
| method | license | citation |
|---|---|---|
| EpiDISH | GPL-2 | Teschendorff, A.E., Breeze, C.E., Zheng, S.C. et al. A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies. BMC Bioinformatics 18, 105 (2017). https://doi.org/10.1186/s12859-017-1511-5 |
| Houseman (Flow.Sorted.Blood) | GPL-3 | Houseman, E.A., Accomando, W.P., Koestler, D.C. et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86 (2012). https://doi.org/10.1186/1471-2105-13-86 Koestler, D.C., Jones, M.J., Usset, J. et al. Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL).BMC Bioinformatics 17, 120 (2016). https://doi.org/10.1186/s12859-016-0943-7 Salas, L.A., Koestler, D.C., Butler, R.A. et al. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biol 19, 64 (2018). https://doi.org/10.1186/s13059-018-1448-7 |
| MethAtlas | Yissum Software Research License "This software is distributed under the Yissum Software Research License, which permits use, modification, and distribution for research purposes only. Commercial use requires a separate license from Yissum (software@yissum.co.il)." | Moss, J., Magenheim, J., Neiman, D. et al. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat Commun 9, 5068 (2018). https://doi.org/10.1038/s41467-018-07466-6 |
| methylCC | GPL-3 | Hicks, S.C., Irizarry, R.A. methylCC: technology-independent estimation of cell type composition using differentially methylated regions. Genome Biol 20, 261 (2019). https://doi.org/10.1186/s13059-019-1827-8 |
| methylResolver | GPL-3 | Arneson, D., Yang, X. & Wang, K. MethylResolver—a method for deconvoluting bulk DNA methylation profiles into known and unknown cell contents. Commun Biol 3, 422 (2020). https://doi.org/10.1038/s42003-020-01146-2 |
You can install methylDeconv from GitHub, we recommend to use the pak package manager:
# install the `pak` package manager
install.packages("pak")
pak::pkg_install("omnideconv/methylDeconv")methylDeconv can either 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.
Both cases will be demonstrated here using 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')
result_raw <- methylDeconv::run_epidish(beta_matrix = beta_matrix, mode='RPC')
# you can also run multiple methods at the same time and get their results + aggregated results:
result_multiple <- methylDeconv::deconvolute_combined(methyl_set = methyl_set,
methods = c('epidish','houseman'),
array = '450k')