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

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

Advanced user deconvolution CP/QP, CIBERSORT and/or RPC deconvolution


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)
}
#> Warning: 'memory.limit()' is Windows-specific
#>         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

# 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

# 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)
}

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))
#> snapshotDate(): 2022-10-24
#> snapshotDate(): 2022-10-24
#> 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.
# 
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()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.5 LTS
#> 
#> Matrix products: default
#> BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
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#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
#> [8] methods   base     
#> 
#> other attached packages:
#>  [1] IlluminaHumanMethylation450kmanifest_0.4.0         
#>  [2] FlowSorted.Blood.450k_1.36.0                       
#>  [3] IlluminaHumanMethylationEPICanno.ilm10b4.hg19_0.6.0
#>  [4] IlluminaHumanMethylationEPICmanifest_0.3.0         
#>  [5] FlowSorted.Blood.EPIC_2.2.0                        
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#> 
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#> [115] DelayedMatrixStats_1.20.0     illuminaio_0.40.0            
#> [117] shiny_1.7.3                   restfulr_0.0.15

References

LA Salas et al. (2018). An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray. Genome Biology 19, 64. doi: 10.1186/s13059-018-1448-7.

LA Salas et al. (2022). . Nat Comm (In Press). doi (biorxiv): 10.1101/2021.04.11.439377.

DC Koestler et al. (2016). Improving cell mixture deconvolution by identifying optimal DNA methylation libraries (IDOL). BMC bioinformatics. 17, 120. doi: 10.1186/s12859-016-0943-7.

K Gervin, LA Salas et al. (2019) . Clin Epigenetics 11,125. doi: 10.1186/s13148-019-0717-y.

EA Houseman et al. (2012) DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86. doi: 10.1186/1471-2105-13-86.

minfi Tools to analyze & visualize Illumina Infinium methylation arrays.

FlowSorted.Blood.EPIC

Lucas A Salas

2022-11-03

Advanced user deconvolution CP/QP, CIBERSORT and/or RPC deconvolution