Lucas A Salas


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


hub <- ExperimentHub()  

query(hub, "FlowSorted.Blood.EPIC")  

FlowSorted.Blood.EPIC <- hub[["EH1136"]]  


The raw dataset is hosted in both ExperimentHub (EH1136) and GEO GSE110554

  1. IDOLOptimizedCpGs the IDOL L-DMR library for EPIC arrays
  1. IDOLOptimizedCpGs450klegacy the IDOL L-DMR library for legacy 450k arrays

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  

hub <- ExperimentHub()  
query(hub, "FlowSorted.Blood.EPIC")  
FlowSorted.Blood.EPIC <- hub[["EH1136"]]  

# 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 = "_")  

# 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 =   
                                referenceset = NULL,  
                                IDOLOptimizedCpGs =IDOLOptimizedCpGs,   
                                returnAll = FALSE)  


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.

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.

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.