## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----eval=TRUE----------------------------------------------------------------
library(FlowSorted.Blood.EPIC)
if (memory.limit()>8000){
FlowSorted.Blood.EPIC <- libraryDataGet('FlowSorted.Blood.EPIC')
FlowSorted.Blood.EPIC
}
## ----eval=FALSE---------------------------------------------------------------
# data("IDOLOptimizedCpGs")
# head(IDOLOptimizedCpGs)
## ----eval=FALSE---------------------------------------------------------------
# data("IDOLOptimizedCpGs450klegacy")
# head(IDOLOptimizedCpGs450klegacy)
## ----eval=TRUE----------------------------------------------------------------
# Step 1: Load the reference library to extract the artificial mixtures
if (memory.limit()>8000){
FlowSorted.Blood.EPIC <- libraryDataGet('FlowSorted.Blood.EPIC')
FlowSorted.Blood.EPIC
}
FlowSorted.Blood.EPIC
library(minfi)
# Note: If your machine does not allow access to internet you can download
# and save the file. Then load it manually into the R environment
# Step 2 separate the reference from the testing dataset if you want to run
# examples for estimations for this function example
RGsetTargets <- FlowSorted.Blood.EPIC[,
FlowSorted.Blood.EPIC$CellType == "MIX"]
sampleNames(RGsetTargets) <- paste(RGsetTargets$CellType,
seq_len(dim(RGsetTargets)[2]), sep = "_")
RGsetTargets
# Step 3: use your favorite package for deconvolution.
# Deconvolute a target data set consisting of EPIC DNA methylation
# data profiled in blood, using your prefered method.
# You can use our IDOL optimized DMR library for the EPIC array. This object
# contains a vector of length 450 consisting of the IDs of the IDOL optimized
# CpG probes. These CpGs are used as the backbone for deconvolution and were
# selected because their methylation signature differs across the six normal
# leukocyte subtypes. Use the option "IDOL"
head (IDOLOptimizedCpGs)
# If you need to deconvolute a 450k legacy dataset use
# IDOLOptimizedCpGs450klegacy instead
# We recommend using Noob processMethod = "preprocessNoob" in minfi for the
# target and reference datasets.
# Cell types included are "CD8T", "CD4T", "NK", "Bcell", "Mono", "Neu"
# To use the IDOL optimized list of CpGs (IDOLOptimizedCpGs) use
# estimateCellCounts2 an adaptation of the popular estimateCellCounts in
# minfi. This function also allows including customized reference arrays.
# Do not run with limited RAM the normalization step requires a big amount
# of memory resources
if (memory.limit()>8000){
propEPIC<-estimateCellCounts2(RGsetTargets, compositeCellType = "Blood",
processMethod = "preprocessNoob",
probeSelect = "IDOL",
cellTypes = c("CD8T", "CD4T", "NK", "Bcell",
"Mono", "Neu"))
print(head(propEPIC$prop))
percEPIC<-round(propEPIC$prop*100,1)
}
## ----eval=TRUE----------------------------------------------------------------
noobset<- preprocessNoob(RGsetTargets)
#or from estimateCellCounts2 returnAll=TRUE
if (memory.limit()>8000){
propEPIC<-projectCellType_CP (
getBeta(noobset)[IDOLOptimizedCpGs,],
IDOLOptimizedCpGs.compTable, contrastWBC=NULL, nonnegative=TRUE,
lessThanOne=FALSE)
print(head(propEPIC))
percEPIC<-round(propEPIC*100,1)
}
# If you prefer CIBERSORT or RPC deconvolution use EpiDISH or similar
# Example not to run
# library(EpiDISH)
# RPC <- epidish(getBeta(noobset)[IDOLOptimizedCpGs,],
# IDOLOptimizedCpGs.compTable, method = "RPC")
# RPC$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$estF*100,1)#percentages
## ----Umbilical Cord Blood, eval=FALSE-----------------------------------------
# # UMBILICAL CORD BLOOD DECONVOLUTION
#
# library (FlowSorted.CordBloodCombined.450k)
# # Step 1: Load the reference library to extract the umbilical cord samples
# FlowSorted.CordBloodCombined.450k <-
# libraryDataGet('FlowSorted.CordBloodCombined.450k')
#
# FlowSorted.CordBloodCombined.450k
#
# # Step 2 separate the reference from the testing dataset if you want to run
# # examples for estimations for this function example
#
# RGsetTargets <- FlowSorted.CordBloodCombined.450k[,
# FlowSorted.CordBloodCombined.450k$CellType == "WBC"]
# sampleNames(RGsetTargets) <- paste(RGsetTargets$CellType,
# seq_len(dim(RGsetTargets)[2]), sep = "_")
# RGsetTargets
#
# # Step 3: use your favorite package for deconvolution.
# # Deconvolute a target data set consisting of 450K DNA methylation
# # data profiled in blood, using your prefered method.
# # You can use our IDOL optimized DMR library for the Cord Blood, This object
# # contains a vector of length 517 consisting of the IDs of the IDOL optimized
# # CpG probes. These CpGs are used as the backbone for deconvolution and were
# # selected because their methylation signature differs across the six normal
# # leukocyte subtypes plus the nucleated red blood cells.
#
# # We recommend using Noob processMethod = "preprocessNoob" in minfi for the
# # target and reference datasets.
# # Cell types included are "CD8T", "CD4T", "NK", "Bcell", "Mono", "Gran",
# # "nRBC"
# # To use the IDOL optimized list of CpGs (IDOLOptimizedCpGsCordBlood) use
# # estimateCellCounts2 from FlowSorted.Blood.EPIC.
# # Do not run with limited RAM the normalization step requires a big amount
# # of memory resources. Use the parameters as specified below for
# # reproducibility.
# #
# if (memory.limit()>8000){
# propUCB<-estimateCellCounts2(RGsetTargets,
# compositeCellType =
# "CordBloodCombined",
# processMethod = "preprocessNoob",
# probeSelect = "IDOL",
# cellTypes = c("CD8T", "CD4T", "NK",
# "Bcell", "Mono", "Gran", "nRBC"))
#
# head(propUCB$prop)
# percUCB<-round(propUCB$prop*100,1)
# }
## ----cell counts, eval=TRUE---------------------------------------------------
library(FlowSorted.Blood.450k)
RGsetTargets2 <- FlowSorted.Blood.450k[,
FlowSorted.Blood.450k$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, eval=FALSE---------------------------------
# # Blood Extended deconvolution or any external reference
# #please contact <Technology.Transfer@dartmouth.edu>
#
# # 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.
# # Deconvolute 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.
# #
# if (memory.limit()>8000){
# 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()