minfi (version 1.18.4)

estimateCellCounts: Cell Proportion Estimation

Description

Estimates the relative proportion of pure cell types within a sample. For example, given peripheral blood samples, this function will return the relative proportions of lymphocytes, monocytes, B-cells, and neutrophils.

Usage

estimateCellCounts(rgSet, compositeCellType = "Blood", processMethod = "auto", probeSelect = "auto", cellTypes = c("CD8T","CD4T", "NK","Bcell","Mono","Gran"), returnAll = FALSE, meanPlot = FALSE, verbose = TRUE, ...)

Arguments

rgSet

The input RGChannelSet for the procedure.

compositeCellType

Which composite cell type is being deconvoluted. Should be one of "Blood", "CordBlood", or "DLPFC". See details.

processMethod

How should the user and reference data be processed together? Default input "auto" will use preprocessQuantile for Blood and DLPFC and preprocessNoob otherwise, in line with the existing literature. Set it to the name of a preprocessing function as a character if you want to override it, like "preprocessFunnorm".

probeSelect

How should probes be selected to distinguish cell types? Options include "both", which selects an equal number (50) of probes (with F-stat p-value < 1E-8) with the greatest magnitude of effect from the hyper- and hypo-methylated sides, and "any", which selects the 100 probes (with F-stat p-value < 1E-8) with the greatest magnitude of difference regardless of direction of effect. Default input "auto" will use "any" for cord blood and "both" otherwise, in line with previous versions of this function and/or our recommendations. Please see the references for more details.

cellTypes

Which cell types, from the reference object, should be we use for the deconvolution? See details.

returnAll

Should the composition table and the normalized user supplied data be return?

verbose

Should the function be verbose?

meanPlot

Whether to plots the average DNA methylation across the cell-type discrimating probes within the mixed and sorted samples.

...

Passed to preprocessQuantile.

Value

Matrix of composition estimates across all samples and cell types.If returnAll=TRUE a list of a count matrix (see previous paragraph), a composition table and the normalized user data in form of a GenomicMethylSet.

Details

This is an implementaion of the Houseman et al (2012) regression calibration approachalgorithm to the Illumina 450k microarray for deconvoluting heterogeneous tissue sources like blood. For example, this function will take an RGChannelSet from a DNA methylation (DNAm) study of blood, and return the relative proportions of CD4+ and CD8+ T-cells, natural killer cells, monocytes, granulocytes, and b-cells in each sample.

The function currently supports cell composition estimation for blood, cord blood, and the frontal cortex, through compositeCellType values of "Blood", "CordBlood", and "DLPFC", respectively. Packages containing the appropriate reference data should be installed before running the function for the first time ("FlowSorted.Blood.450k", "FlowSorted.DLPFC.450k", "FlowSorted.CordBlood.450k"). Each tissue supports the estimation of different cell types, delimited via the cellTypes argument. For blood, these are "Bcell", "CD4T", "CD8T", "Eos", "Gran", "Mono", "Neu", and "NK" (though the default value for cellTypes is often sufficient). For cord blood, these are "Bcell", "CD4T", "CD8T", "Gran", "Mono", "Neu", and "nRBC". For frontal cortex, these are "NeuN_neg" and "NeuN_pos". See documentation of individual reference packages for more details.

The meanPlot should be used to check for large batch effects in the data, reducing the confidence placed in the composition estimates. This plot depicts the average DNA methylation across the cell-type discrimating probes in both the provided and sorted data. The means from the provided heterogeneous samples should be within the range of the sorted samples. If the sample means fall outside the range of the sorted means, the cell type estimates will inflated to the closest cell type. Note that we quantile normalize the sorted data with the provided data to reduce these batch effects.

References

EA Houseman, WP Accomando, DC Koestler, BC Christensen, CJ Marsit, HH Nelson, JK Wiencke and KT Kelsey. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC bioinformatics (2012) 13:86. doi:10.1186/1471-2105-13-86. AE Jaffe and RA Irizarry. Accounting for cellular heterogeneity is critical in epigenome-wide association studies. Genome Biology (2014) 15:R31. doi:10.1186/gb-2014-15-2-r31. KM Bakulski, JI Feinberg, SV Andrews, J Yang, S Brown, S McKenney, F Witter, J Walston, AP Feinberg, and MD Fallin. DNA methylation of cord blood cell types: Applications for mixed cell birth studies. Manuscript in review.

See Also

preprocessQuantile

Examples

## Not run: 
# if(require(FlowSorted.Blood.450k)) {
#   wh.WBC <- which(FlowSorted.Blood.450k$CellType == "WBC")
#   wh.PBMC <- which(FlowSorted.Blood.450k$CellType == "PBMC")
#   RGset <- FlowSorted.Blood.450k[, c(wh.WBC, wh.PBMC)]
#   ## The following line is purely to work around an issue with repeated
#   ## sampleNames and Biobase::combine()
#   sampleNames(RGset) <- paste(RGset$CellType,
#     c(seq(along = wh.WBC), seq(along = wh.PBMC)), sep = "_")
#   counts <- estimateCellCounts(RGset, meanPlot = FALSE)
#   round(counts, 2)
# }
# ## End(Not run)