projectCellType_CP: projectCellType_CP

In immunomethylomics/FlowSorted.Blood.EPIC: Illumina EPIC data on immunomagnetic sorted peripheral adult blood cells

projectCellType_CP

Description

This function predicts the underlying cellular composition of heterogeneous tissue types (i.e., WB) using the constrained projection procedure described by Houseman et al. 2012. This is equivalent to the internal projectCellType function in minfi, Jaffe et al. 2014. We recommend this function only for advanced users. Please preprocess your dataset filtering potential bad quality samples.

Usage

projectCellType_CP(
  Y,
  coefWBC,
  contrastWBC = NULL,
  nonnegative = TRUE,
  lessThanOne = FALSE
)

Arguments

Y	A J x N matrix of methylation beta-values collected from mixed/ heterogeneous biospecimen (i.e., Whole Blood). Target set.
coefWBC	A J x K projection matrix;, i.e., within-cell type mean methylation matrix across J L-DMRs and K number of cell types
contrastWBC	Contrast for cell composition predictions set to NULL by default. The user should not modify this
nonnegative	Should cell predictions be nonnegative. Defaults to TRUE
lessThanOne	Should the predictions sum less than one. Default is FALSE

Value

A N x K matrix of cell proportion estimates across the K cell types for each of the N subjects contained in the Target Set.

Examples

# Step 1: Load the reference library to extract the artificial mixtures
FlowSorted.Blood.EPIC <- libraryDataGet("FlowSorted.Blood.EPIC")
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: apply the CP approach using the preloaded matrix of IDOL.
# 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 matrix of dimensions 450 X 6 consisting on average methylation
# values obtained from 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.

head(IDOLOptimizedCpGs.compTable)
# If you need to deconvolute a 450k legacy dataset use
# IDOLOptimizedCpGs450klegacy.compTable 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"

# If you need to normalize your data do not run with limited RAM. The
# normalization step requires a big amount of memory resources

if (memory.limit() > 8000) {
    propEPIC <- projectCellType_CP(
        getBeta(preprocessNoob(RGsetTargets))[IDOLOptimizedCpGs, ],
        IDOLOptimizedCpGs.compTable,
        contrastWBC = NULL, nonnegative = TRUE,
        lessThanOne = FALSE
    )

    head(propEPIC)
    percEPIC <- round(propEPIC * 100, 1)
}

immunomethylomics/FlowSorted.Blood.EPIC documentation built on May 24, 2023, 2:22 a.m.