HiTIMED: Hierarchical Tumor Immune Microenvironment Epigenetic Deconvolution for accurate cell type resolution in the tumor microenvironment using tumor-type-specific DNA methylation data
The HiTIMED deconvolution estimates proportions up to 17 cell types (tumor, epithelial, endothelial, stromal, basophil, eosinophil, neutrophil, dendritic cell, monocyte, B naïve, B memory, CD4T naïve, CD4T memory, CD8T naïve, CD8T memory, T regulatory, and natural killer cells) in 3 major tumor microenvironment components (tumor, immune, angiogenic). Recommended methylation data preprocess order: pOOBAH --> Background subtraction using oob (noob) --> Dye bias correction. The manuscript is published at https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-022-03736-6. This work is recognized by the Neukom Award and we appreciate the support from the Dartmouth Neukom Institute (https://neukom.dartmouth.edu/research/neukom-research-prizes/2022-research-prize-winners).
devtools::install_github("SalasLab/HiTIMED")
library(HiTIMED)
library(FlowSorted.Blood.EPIC)
library(dplyr)
Example_Beta<-query(ExperimentHub(), "HiTIMED")[["EH8092"]]
HiTIMED_result<-HiTIMED_deconvolution(Example_Beta,"COAD",6,"tumor")
head(HiTIMED_result)
library(IlluminaHumanMethylationEPICv2anno.20a1.hg38)
library(IlluminaHumanMethylationEPICv2manifest)
v2_RGset = read.metharray.exp("dir",recursive = TRUE)
annotation(v2_RGset)["array"] = "IlluminaHumanMethylationEPICv2" #Update annotation files for v2
annotation(v2_RGset)["annotation"] = "20a1.hg38"
v2_MSet <-preprocessNoob(v2_RGset)
v2_Betas<-getBeta(v2_MSet)
v2_Betas<- sesame::betasCollapseToPfx(v2_Betas)
HiTIMED_deconvolution(v2_Betas,"COAD",6,"tumor")
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