## ----setup, include = FALSE---------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----eval=FALSE---------------------------------------------------------- # library(ExperimentHub) # # hub <- ExperimentHub() # # query(hub, "FlowSorted.Blood.EPIC") # # FlowSorted.Blood.EPIC <- hub[["EH1136"]] # # FlowSorted.Blood.EPIC ## ----eval=FALSE---------------------------------------------------------- # head(IDOLOptimizedCpGs) ## ----eval=FALSE---------------------------------------------------------- # head(IDOLOptimizedCpGs450klegacy) ## ----eval=FALSE---------------------------------------------------------- # # Step 1: Load the reference library to extract the artificial mixtures # # library(ExperimentHub) # hub <- ExperimentHub() # query(hub, "FlowSorted.Blood.EPIC") # FlowSorted.Blood.EPIC <- hub[["EH1136"]] # FlowSorted.Blood.EPIC # # # Step 2 separate the reference from the testing dataset # # RGsetTargets <- FlowSorted.Blood.EPIC[, # FlowSorted.Blood.EPIC$CellType == "MIX"] # # sampleNames(RGsetTargets) <- paste(RGsetTargets$CellType, # seq_len(dim(RGsetTargets)[2]), sep = "_") # RGsetTargets # # # Step 3: Deconvolute using the IDOL L-DMR # # head (IDOLOptimizedCpGs) # # # If you need to deconvolute a 450k legacy dataset use # # IDOLOptimizedCpGs450klegacy instead # # # Do not run with limited RAM the normalization step requires a big amount of # # memory resources # # if (memory.limit()>8000){ # countsEPIC<-estimateCellCounts2(RGsetTargets, compositeCellType = "Blood", # processMethod = "preprocessNoob", # probeSelect = "IDOL", # cellTypes = c("CD8T", "CD4T", "NK", "Bcell", # "Mono", "Neu"), # referencePlatform = # "IlluminaHumanMethylationEPIC", # referenceset = NULL, # IDOLOptimizedCpGs =IDOLOptimizedCpGs, # returnAll = FALSE) # # head(countsEPIC$counts) # }