The methylCC user's guide

opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE)


There are several approaches available to adjust for differents in the relative proportion of cell types in whole blood measured from DNA methylation (DNAm). For example, reference-based approaches require the use of reference data sets made up of purified cell types to identify cell type-specific DNAm signatures. These cell type-specific DNAm signatures are used to estimate the relative proportions of cell types directly, but these reference data sets are laborious and expensive to collect. Furthermore, these reference data sets will need to be continuously collected over time as new platform technologies emerge measuring DNAm because the observed methylation levels for the same CpGs in the same sample vary depending the platform technology.

In contrast, there are reference-free approaches, which are based on methods related to surrogate variable analysis or linear mixed models. These approaches do not provide estimates of the relative proportions of cell types, but rather these methods just remove the variability induced from the differences in relative cell type proportions in whole blood samples.

Here, we present a statistical model that estimates the cell composition of whole blood samples measured from DNAm. The method can be applied to microarray or sequencing data (for example whole-genome bisulfite sequencing data, WGBS, reduced representation bisulfite sequencing data, RRBS). Our method is based on the idea of identifying informative genomic regions that are clearly methylated or unmethylated for each cell type, which permits estimation in multiple platform technologies as cell types preserve their methylation state in regions independent of platform despite observed measurements being platform dependent.

Getting Started

Load the methylCC R package and other packages that we'll need later on.



Whole Blood Illumina 450k Microarray Data Example

# Phenotypic information about samples

# RGChannelSet
rgset <- FlowSorted.Blood.450k[,
                pData(FlowSorted.Blood.450k)$CellTypeLong %in% "Whole blood"]

Using the estimatecc() function

Input for estimatecc()

The estimatecc() function must have one object as input:

  1. an object such as an RGChannelSet from the R package minfi or a BSseq object from the R package bsseq. This object should contain observed DNAm levels at CpGs (rows) in a set of $N$ whole blood samples (columns).

Running estimatecc()

In this example, we are interested in estimating the cell composition of the whole blood samples listed in the FlowSorted.Blood.450k R/Bioconductor package. To run the methylcC::estimatecc() function, just provide the RGChannelSet. This will create an estimatecc object. We will call the object est.

est <- estimatecc(object = rgset) 

To see the cell composition estimates, use the cell_counts() function.


Compare to minfi::estimateCellCounts()

We can also use the estimateCellCounts() from R/Bioconductor package minfi to estimate the cell composition for each of the whole blood samples.

sampleNames(rgset) <- paste0("Sample", 1:6)

est_minfi <- minfi::estimateCellCounts(rgset)

Then, we can compare the estimates to methylCC::estimatecc().

df_minfi = gather(cbind("samples" = rownames(cell_counts(est)),
                  celltype, est, -samples)

df_methylCC = gather(cbind("samples" = rownames(cell_counts(est)),
                     celltype, est, -samples)

dfcombined <- full_join(df_minfi, df_methylCC, 
                               by = c("samples", "celltype"))

ggplot(dfcombined, aes(x=est.x, y = est.y, color = celltype)) +
    geom_point() + xlim(0,1) + ylim(0,1) +
    geom_abline(intercept = 0, slope = 1) +
    xlab("Using minfi::estimateCellCounts()") + 
    ylab("Using methylCC::estimatecc()") +
    labs(title = "Comparing cell composition estimates")

We see the estimates closely match for the six cell types.



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methylCC documentation built on Nov. 8, 2020, 7:35 p.m.