Illumina Human Methylation data from EPIC on immunomagnetic sorted adult blood cell populations. The FlowSorted.Blood.EPIC package contains Illumina HumanMethylationEPIC (EPIC)) DNA methylation microarray data from the immunomethylomics group (manuscript submitted), consisting of 37 magnetic sorted blood cell references and 12 samples, formatted as an RGChannelSet object for integration and normalization using most of the existing Bioconductor packages.
This package contains data similar to the FlowSorted.Blood.450k package consisting of data from peripheral blood samples generated from adult men and women. However, when using the newer EPIC microarray minfi estimates of cell type composition using the FlowSorted.Blood.450k package are less precise compared to actual cell counts. Hence, this package consists of appropriate data for deconvolution of adult blood samples used in for example EWAS relying in the newer EPIC technology.
Researchers may find this package useful as these samples represent different cellular populations ( T lymphocytes (CD4+ and CD8+), B cells (CD19+), monocytes (CD14+), NK cells (CD56+) and Neutrophils of cell sorted blood generated with high purity estimates. As a test of accuracy 12 experimental mixtures were reconstructed using fixed amounts of DNA from purified cells.
Objects included:
1. FlowSorted.Blood.EPIC is the RGChannelSet object containing the reference library
library(FlowSorted.Blood.EPIC)
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if (memory.limit()>8000){
FlowSorted.Blood.EPIC <- libraryDataGet('FlowSorted.Blood.EPIC')
FlowSorted.Blood.EPIC
}
#> Warning: 'memory.limit()' is Windows-specific
#> see ?FlowSorted.Blood.EPIC and browseVignettes('FlowSorted.Blood.EPIC') for documentation
#> loading from cache
#> class: RGChannelSet
#> dim: 1051815 49
#> metadata(0):
#> assays(2): Green Red
#> rownames(1051815): 1600101 1600111 ... 99810990 99810992
#> rowData names(0):
#> colnames(49): 201868500150_R01C01 201868500150_R03C01 ...
#> 201870610111_R06C01 201870610111_R07C01
#> colData names(32): Sample_Plate Sample_Well ... filenames normalmix
#> Annotation
#> array: IlluminaHumanMethylationEPIC
#> annotation: ilm10b4.hg19The raw dataset is hosted in both ExperimentHub (EH1136) and GEO GSE110554
See the object help files for additional information
estimateCellCounts2 function for cell type deconvolution:
We offer the function estimateCellCounts2 a modification of the popular estimatesCellCounts function in minfi. Our function corrected small glitches when dealing with combining the DataFrame objects with the reference libraries. We allow the use of MethylSet objects such as those from GEO. However, we offer only Quantile normalization for those datasets (assuming that they have not been previously normalized). The estimates are calculated using the CP/QP method described in Houseman et al. 2012. and adapted in minfi. CIBERSORT and RPC are allowed using external packages.
see ?estimateCellCounts2 for details
# Step 1: Load the reference library to extract the artificial mixtures
FlowSorted.Blood.EPIC <- libraryDataGet('FlowSorted.Blood.EPIC')
#> see ?FlowSorted.Blood.EPIC and browseVignettes('FlowSorted.Blood.EPIC') for documentation
#> loading from cache
FlowSorted.Blood.EPIC
#> class: RGChannelSet
#> dim: 1051815 49
#> metadata(0):
#> assays(2): Green Red
#> rownames(1051815): 1600101 1600111 ... 99810990 99810992
#> rowData names(0):
#> colnames(49): 201868500150_R01C01 201868500150_R03C01 ...
#> 201870610111_R06C01 201870610111_R07C01
#> colData names(32): Sample_Plate Sample_Well ... filenames normalmix
#> Annotation
#> array: IlluminaHumanMethylationEPIC
#> annotation: ilm10b4.hg19
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
#> class: RGChannelSet
#> dim: 1051815 12
#> metadata(0):
#> assays(2): Green Red
#> rownames(1051815): 1600101 1600111 ... 99810990 99810992
#> rowData names(0):
#> colnames(12): MIX_1 MIX_2 ... MIX_11 MIX_12
#> colData names(32): Sample_Plate Sample_Well ... filenames normalmix
#> Annotation
#> array: IlluminaHumanMethylationEPIC
#> annotation: ilm10b4.hg19
# 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)
#> [1] "cg08769189" "cg07661835" "cg00219921" "cg13468685" "cg04329870"
#> [6] "cg14085952"
# 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
propEPIC<-estimateCellCounts2(RGsetTargets, compositeCellType = "Blood",
processMethod = "preprocessNoob",
probeSelect = "IDOL",
cellTypes = c("CD8T", "CD4T", "NK", "Bcell",
"Mono", "Neu"))
#> see ?FlowSorted.Blood.EPIC and browseVignettes('FlowSorted.Blood.EPIC') for documentation
#> loading from cache
#> [estimateCellCounts2] Combining user data with reference (flow sorted) data.
#> [estimateCellCounts2] Processing user and reference data together.
#> Loading required package: IlluminaHumanMethylationEPICmanifest
#> Loading required package: IlluminaHumanMethylationEPICanno.ilm10b4.hg19
#> [estimateCellCounts2] Using IDOL L-DMR probes for composition estimation.
#> [estimateCellCounts2] Estimating proportion composition (prop), if you provide cellcounts those will be provided as counts in the composition estimation.
print(head(propEPIC$prop))
#> CD8T CD4T NK Bcell Mono Neu
#> MIX_1 0.1916 0.0708 0.1515 0.1904 0.1901 0.2118
#> MIX_2 0.0463 0.1763 0.0179 0.0442 0.0582 0.6699
#> MIX_3 0.0675 0.1012 0.0043 0.0223 0.1092 0.7033
#> MIX_4 0.1208 0.1781 0.0200 0.0167 0.0764 0.6032
#> MIX_5 0.2891 0.1603 0.1525 0.0718 0.2227 0.1126
#> MIX_6 0.0963 0.1576 0.0269 0.0243 0.0703 0.6401
percEPIC<-round(propEPIC$prop*100,1)
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 0.1916 0.0708 0.1515 0.1904 0.1901 0.2118
#> MIX_2 0.0463 0.1763 0.0179 0.0442 0.0582 0.6699
#> MIX_3 0.0675 0.1012 0.0043 0.0223 0.1092 0.7033
#> MIX_4 0.1208 0.1781 0.0200 0.0167 0.0764 0.6032
#> MIX_5 0.2891 0.1603 0.1525 0.0718 0.2227 0.1126
#> MIX_6 0.0963 0.1576 0.0269 0.0243 0.0703 0.6401
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)#percentagesUmbilical Cord Blood
# # 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.
# #
# 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)Using cell counts instead of proportions. Note: These are random numbers, not the actual cell counts of the experiment
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
#> class: RGChannelSet
#> dim: 622399 6
#> metadata(0):
#> assays(2): Green Red
#> rownames(622399): 10600313 10600322 ... 74810490 74810492
#> rowData names(0):
#> colnames(6): WBC_1 WBC_2 ... WBC_5 WBC_6
#> colData names(8): Sample_Name Slide ... CellType Sex
#> Annotation
#> array: IlluminaHumanMethylation450k
#> annotation: ilmn12.hg19
propEPIC2<-estimateCellCounts2(RGsetTargets2, compositeCellType = "Blood",
processMethod = "preprocessNoob",
probeSelect = "IDOL",
cellTypes = c("CD8T", "CD4T", "NK", "Bcell",
"Mono", "Neu"), cellcounts = rep(10000,6))
#> see ?FlowSorted.Blood.EPIC and browseVignettes('FlowSorted.Blood.EPIC') for documentation
#> loading from cache
#> [convertArray] Casting as IlluminaHumanMethylationEPIC
#> Loading required package: IlluminaHumanMethylation450kmanifest
#> [estimateCellCounts2] Combining user data with reference (flow sorted) data.
#> Warning in asMethod(object): NAs introduced by coercion
#> [estimateCellCounts2] Processing user and reference data together.
#> [estimateCellCounts2] Using IDOL L-DMR probes for composition estimation.
#> [estimateCellCounts2] Estimating proportion composition (prop), if you provide cellcounts those will be provided as counts in the composition estimation.
head(propEPIC2$prop)
#> CD8T CD4T NK Bcell Mono Neu
#> WBC_1 0.1458 0.2032 0.0973 0.0397 0.0999 0.4388
#> WBC_2 0.0513 0.2054 0.0718 0.0651 0.1317 0.5053
#> WBC_3 0.1228 0.1680 0.0466 0.0335 0.0750 0.5835
#> WBC_4 0.0892 0.1843 0.0516 0.0797 0.0677 0.5563
#> WBC_5 0.0298 0.1570 0.0326 0.0315 0.0636 0.7110
#> WBC_6 0.0787 0.1934 0.0503 0.0545 0.0708 0.5803
head(propEPIC2$counts)
#> CD8T CD4T NK Bcell Mono Neu
#> WBC_1 1458 2032 973 397 999 4388
#> WBC_2 513 2054 718 651 1317 5053
#> WBC_3 1228 1680 466 335 750 5835
#> WBC_4 892 1843 516 797 677 5563
#> WBC_5 298 1570 326 315 636 7110
#> WBC_6 787 1934 503 545 708 5803
percEPIC2<-round(propEPIC2$prop*100,1)Blood Extended deconvolution
# # Blood Extended deconvolution or any external reference
# #please contact <Technology.Transfer@dartmouth.edu>
#
# # Do not run
# library (FlowSorted.BloodExtended.EPIC)
# #
# # Step 1: Extract the mix samples
#
# FlowSorted.Blood.EPIC <- libraryDataGet('FlowSorted.Blood.EPIC')
#
# # Step 2 separate the reference from the testing dataset if you want to run
# # examples for estimations for this function example
#
# RGsetTargets <- FlowSorted.Blood.EPIC[,
# FlowSorted.Blood.EPIC$CellType == "MIX"]
# sampleNames(RGsetTargets) <- paste(RGsetTargets$CellType,
# seq_len(dim(RGsetTargets)[2]), sep = "_")
# RGsetTargets
#
# # Step 3: use your favorite package for deconvolution.
# # Deconvolute the target data set 450K or EPIC blood DNA methylation.
# # We recommend ONLY the IDOL method, the automatic method can lead to severe
# # biases.
#
# # We recommend using Noob processMethod = "preprocessNoob" in minfi for the
# # target and reference datasets.
# # Cell types included are "Bas", "Bmem", "Bnv", "CD4mem", "CD4nv",
# # "CD8mem", "CD8nv", "Eos", "Mono", "Neu", "NK", and "Treg"
# # Use estimateCellCounts2 from FlowSorted.Blood.EPIC.
# # Do not run with limited RAM the normalization step requires a big amount
# # of memory resources. Use the parameters as specified below for
# # reproducibility.
# #
# prop_ext<-estimateCellCounts2(RGsetTargets,
# compositeCellType =
# "BloodExtended",
# processMethod = "preprocessNoob",
# probeSelect = "IDOL",
# cellTypes = c("Bas", "Bmem", "Bnv",
# "CD4mem", "CD4nv",
# "CD8mem", "CD8nv", "Eos",
# "Mono", "Neu", "NK", "Treg"),
# CustomCpGs =if(RGsetTargets@annotation[1]=="IlluminaHumanMethylationEPIC"){
# IDOLOptimizedCpGsBloodExtended}else{IDOLOptimizedCpGsBloodExtended450k})
#
# perc_ext<-round(prop_ext$prop*100,1)
# head(perc_ext)sessionInfo()
#> R version 4.3.0 RC (2023-04-13 r84269)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.2 LTS
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#> [1] IlluminaHumanMethylation450kmanifest_0.4.0
#> [2] FlowSorted.Blood.450k_1.38.0
#> [3] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
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#> [95] yaml_2.3.7 evaluate_0.21
#> [97] codetools_0.2-19 tibble_3.2.1
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#> [103] Rcpp_1.0.10 png_0.1-8
#> [105] XML_3.99-0.14 ellipsis_0.3.2
#> [107] readr_2.1.4 blob_1.2.4
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#> [111] doRNG_1.8.6 sparseMatrixStats_1.12.0
#> [113] bitops_1.0-7 illuminaio_0.42.0
#> [115] purrr_1.0.1 crayon_1.5.2
#> [117] rlang_1.1.1 KEGGREST_1.40.0References
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minfi Tools to analyze & visualize Illumina Infinium methylation arrays.