noobset<- preprocessNoob(RGsetTargets)
#or from estimateCellCounts2 returnAll=TRUE

 propEPIC<-projectCellType_CP (
 getBeta(noobset)[IDOLOptimizedCpGs,],
 IDOLOptimizedCpGs.compTable, contrastWBC=NULL, nonnegative=TRUE,
 lessThanOne=FALSE)

print(head(propEPIC))
#>         CD8T   CD4T     NK  Bcell   Mono    Neu
#> MIX_1 0.1907 0.0710 0.1519 0.1906 0.1907 0.2116
#> MIX_2 0.0455 0.1763 0.0184 0.0445 0.0583 0.6702
#> MIX_3 0.0665 0.1014 0.0049 0.0225 0.1092 0.7037
#> MIX_4 0.1199 0.1783 0.0204 0.0169 0.0765 0.6035
#> MIX_5 0.2883 0.1604 0.1529 0.0720 0.2232 0.1122
#> MIX_6 0.0954 0.1578 0.0272 0.0246 0.0705 0.6404
percEPIC<-round(propEPIC*100,1)


# If you prefer CIBERSORT or RPC deconvolution use EpiDISH or similar

# Example not to run

# library(EpiDISH)
# RPC <- epidish(getBeta(noobset)[IDOLOptimizedCpGs,],
# IDOLOptimizedCpGs.compTable, method = "RPC")
# RPC$estF#RPC proportion estimates
# percEPICRPC<-round(RPC$estF*100,1)#percentages
# 
# CBS <- epidish(getBeta(noobset)[IDOLOptimizedCpGs,],
# IDOLOptimizedCpGs.compTable, method = "CBS")
# CBS$estF#CBS proportion estimates
# percEPICCBS<-round(CBS$estF*100,1)#percentages

# # UMBILICAL CORD BLOOD DECONVOLUTION
# 
# library (FlowSorted.CordBloodCombined.450k)
# # Step 1: Load the reference library to extract the umbilical cord samples
# FlowSorted.CordBloodCombined.450k <- 
#     libraryDataGet('FlowSorted.CordBloodCombined.450k') 
# 
# FlowSorted.CordBloodCombined.450k
# 
# # Step 2 separate the reference from the testing dataset if you want to run 
# # examples for estimations for this function example
# 
# RGsetTargets <- FlowSorted.CordBloodCombined.450k[,
# FlowSorted.CordBloodCombined.450k$CellType == "WBC"]
# sampleNames(RGsetTargets) <- paste(RGsetTargets$CellType,
#                               seq_len(dim(RGsetTargets)[2]), sep = "_")
# RGsetTargets
# 
# # Step 3: use your favorite package for deconvolution.
# # Deconvolve a target data set consisting of 450K DNA methylation 
# # data profiled in blood, using your prefered method.
# # You can use our IDOL optimized DMR library for the Cord Blood,  This object
# # contains a vector of length 517 consisting of the IDs of the IDOL optimized
# # CpG probes.  These CpGs are used as the backbone for deconvolution and were
# # selected because their methylation signature differs across the six normal 
# # leukocyte subtypes plus the nucleated red blood cells.
# 
# # We recommend using Noob processMethod = "preprocessNoob" in minfi for the 
# # target and reference datasets. 
# # Cell types included are "CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran", 
# # "nRBC"
# # To use the IDOL optimized list of CpGs (IDOLOptimizedCpGsCordBlood) use 
# # estimateCellCounts2 from FlowSorted.Blood.EPIC. 
# # Do not run with limited RAM the normalization step requires a big amount 
# # of memory resources. Use the parameters as specified below for 
# # reproducibility.
# # 
# 
#     propUCB<-estimateCellCounts2(RGsetTargets,
#                                     compositeCellType =
#                                                "CordBloodCombined",
#                                     processMethod = "preprocessNoob",
#                                     probeSelect = "IDOL",
#                                     cellTypes = c("CD8T", "CD4T", "NK",
#                                     "Bcell", "Mono", "Gran", "nRBC"))
# 
#     head(propUCB$prop)
#     percUCB<-round(propUCB$prop*100,1)

# library(FlowSorted.Blood.450k)
# RGsetTargets2 <- FlowSorted.Blood.450k[,
#                              FlowSorted.Blood.450k$CellType == "WBC"]
# sampleNames(RGsetTargets2) <- paste(RGsetTargets2$CellType,
#                              seq_len(dim(RGsetTargets2)[2]), sep = "_")
# RGsetTargets2
# propEPIC2<-estimateCellCounts2(RGsetTargets2, compositeCellType = "Blood",
#                              processMethod = "preprocessNoob",
#                              probeSelect = "IDOL",
#                              cellTypes = c("CD8T", "CD4T", "NK", "Bcell",
#                              "Mono", "Neu"), cellcounts = rep(10000,6))
# head(propEPIC2$prop)
# head(propEPIC2$counts)
# percEPIC2<-round(propEPIC2$prop*100,1)

# # Blood Extended deconvolution or any external reference
# #please contact <[email protected]>
# 
# # Do not run
# library (FlowSorted.BloodExtended.EPIC)
# # 
# # Step 1: Extract the mix samples
# 
# FlowSorted.Blood.EPIC <- libraryDataGet('FlowSorted.Blood.EPIC')
# 
# # Step 2 separate the reference from the testing dataset if you want to run 
# # examples for estimations for this function example
# 
# RGsetTargets <- FlowSorted.Blood.EPIC[,
# FlowSorted.Blood.EPIC$CellType == "MIX"]
# sampleNames(RGsetTargets) <- paste(RGsetTargets$CellType,
#                               seq_len(dim(RGsetTargets)[2]), sep = "_")
# RGsetTargets
# 
# # Step 3: use your favorite package for deconvolution.
# # Deconvolve the target data set 450K or EPIC blood DNA methylation. 
# # We recommend ONLY the IDOL method, the automatic method can lead to severe
# # biases.
# 
# # We recommend using Noob processMethod = "preprocessNoob" in minfi for the 
# # target and reference datasets. 
# # Cell types included are "Bas", "Bmem", "Bnv", "CD4mem", "CD4nv", 
# # "CD8mem", "CD8nv", "Eos", "Mono", "Neu", "NK", and "Treg"
# # Use estimateCellCounts2 from FlowSorted.Blood.EPIC. 
# # Do not run with limited RAM the normalization step requires a big amount 
# # of memory resources. Use the parameters as specified below for 
# # reproducibility.
# # 
# 
#     prop_ext<-estimateCellCounts2(RGsetTargets,
#                                     compositeCellType =
#                                                "BloodExtended",
#                                     processMethod = "preprocessNoob",
#                                     probeSelect = "IDOL",
#                                     cellTypes = c("Bas", "Bmem", "Bnv",
#                                                "CD4mem", "CD4nv",
#                                               "CD8mem", "CD8nv", "Eos",
#                                               "Mono", "Neu", "NK", "Treg"),
#     CustomCpGs =if(RGsetTargets@annotation[1]=="IlluminaHumanMethylationEPIC"){
#     IDOLOptimizedCpGsBloodExtended}else{IDOLOptimizedCpGsBloodExtended450k})
# 
#    perc_ext<-round(prop_ext$prop*100,1)
#    head(perc_ext)

sessionInfo()
#> R Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so 
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_GB              LC_COLLATE=C              
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: America/New_York
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
#> [8] methods   base     
#> 
#> other attached packages:
#>  [1] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
#>  [2] IlluminaHumanMethylationEPICmanifest_0.3.0         
#>  [3] FlowSorted.Blood.EPIC_2.11.0                       
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