In immunomethylomics/FlowSorted.BloodExtended.EPIC: Illumina EPIC data on extended immunomagnetic sorted blood populations

knitr::opts_chunk$set(cache = FALSE, warning = FALSE, message = FALSE, 
                        cache.lazy = FALSE,collapse = TRUE, comment = "#>"
)

The FlowSorted.BloodExtended.EPIC package contains data derived from Illumina HumanMethylationEPIC DNA methylation microarrays (Salas LA et al. 2021), consisting of 56 blood cell references and 12 mixed blood samples, formatted as an RGChannelSet object for integration and normalization using most of the existing Bioconductor packages.

The FlowSorted.BloodExtended.EPIC dataset includes information from neutrophils (Neu, n=6), eosinophils (Eos, n=4), basophils (Bas, n=6), monocytes (Mono, n=5), B naive cells (Bnv, n=4), B memory cells (Bmem, n=6), T-helper CD4+ naive cells (CD4nv, n=5), T-helper CD4+ memory cells (CD4mem, n=4), T regulatory cells (Treg, n=3), T-cytotoxic CD8+ naive cells (CD8nv, n=5),T-cytotoxic memory CD8+ cells (CD8mem, n=4), and natural killer cells (NK, n=4), plus 12 DNA artificial mixtures, labeled as MIX in this dataset.

Researchers may find this package useful as these samples represent different cellular populations, including eosinophils, basophils, monocytes, B naive cells, B memory cells, T-helper CD4+ naive cells, T-helper CD4+ memory cells, T regulatory cells, T-cytotoxic CD8+ naive cells, T-cytotoxic memory CD8+ cells and natural killer cells, 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.BloodExtended.EPIC is the RGChannelSet object containing the reference library

library (FlowSorted.BloodExtended.EPIC)
FlowSorted.BloodExtended.EPIC::FlowSorted.BloodExtended.EPIC

The raw dataset is hosted in GEO GSE167998

1. IDOLOptimizedCpGsBloodExtended the IDOL L-DMR library for EPIC arrays

head(IDOLOptimizedCpGsBloodExtended)  

1. IDOLOptimizedCpGsBloodExtended450k the IDOL L-DMR library for legacy 450k arrays

head(IDOLOptimizedCpGsBloodExtended450k)  

Usage for extended deconvolution

# Blood Extended deconvolution or any external reference
#please contact <[e-mail]>

# 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()

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). \emph{Enhanced cell deconvolution of peripheral blood using DNA methylation for high-resolution immune profiling}. Nat Comm 13, 761 (2022). doi: 10.1038/s41467-021-27864-7.

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.

K Gervin, LA Salas et al. (2019) \emph{Systematic evaluation and validation of references and library selection methods for deconvolution of cord blood DNA methylation data}. 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.

immunomethylomics/FlowSorted.BloodExtended.EPIC documentation built on Jan. 29, 2024, 8:43 p.m.