In perishky/meffil: Efficient algorithms for DNA methylation

library(knitr)
library(Cairo)
opts_chunk$set(warning=FALSE, fig.width=6, fig.height=6, dev="CairoPNG", stop=TRUE)

library(minfi)
library(matrixStats)
library(GEOquery)

Comparison of functional normalization implementations

Download example data set

path <- download.450k.demo.dataset()

Load and normalize using minfi

The current version minfi::preprocessFunnorm() contains a bug (Feb 25, 2015). To make a proper comparison with meffil, this bug should be fixed and minfi reinstalled. This can be done with the included patch file: fix-minfi-control-matrix.patch.

Load the data.

library(minfi, quietly=TRUE)

Normalize using the minfi package.

raw.minfi <- read.metharray.exp(base = path)
system.time(norm.minfi <- preprocessFunnorm(raw.minfi, nPCs=2,
                                            sex=NULL, bgCorr=TRUE, dyeCorr=TRUE, verbose=TRUE))

Normalize using meffil

Load the code and sample filename information.

library(meffil)
samplesheet <- meffil.create.samplesheet(path)

The package uses mclapply to speed up computation. Provide the number of processors available.

options(mc.cores=5)

List the cell type references available.

meffil.list.cell.type.references()

Set parameters for the analysis.

qc.file <- "minfi/qc-report.html"
author <- "Prickett, et al."
study <- "Silver-Russell syndrome patients (GEO:GSE55491)"
number.pcs <- 2
norm.file <- "minfi/normalization-report.html"
cell.type.reference <- "blood gse35069"

Normalize using meffil step-by-step

Create QC objects for QC report and later normalization.

qc.objects <- meffil.qc(samplesheet, cell.type.reference=cell.type.reference, verbose=T)

Create the QC report.

qc.summary <- meffil.qc.summary(qc.objects, verbose=T)
meffil.qc.report(qc.summary,
                 output.file=qc.file,
                 author=author,
                 study=study)
````

Remove any samples with too many bad probes.
```r
if (nrow(qc.summary$bad.samples) > 0)
    qc.objects <- meffil.remove.samples(qc.objects, qc.summary$bad.samples$sample.name)

Check how many principal components to include.

print(meffil.plot.pc.fit(qc.objects)$plot)

Normalize sample quantiles in preparation for

norm.objects <- meffil.normalize.quantiles(qc.objects, number.pcs=number.pcs, verbose=T)

Normalize the dataset using the normalized quantiles.

norm.meffil <- meffil.normalize.samples(norm.objects,
                                        just.beta=F, 
                                        cpglist.remove=qc.summary$bad.cpgs$name,
                                        verbose=T)

beta.meffil <- meffil.get.beta(norm.meffil$M, norm.meffil$U)

Compute principal components of the normalized methylation matrix.

pcs <- meffil.methylation.pcs(beta.meffil, sites=meffil.get.autosomal.sites("450k"), verbose=T)

Generate a normalization report.

parameters <- meffil.normalization.parameters(norm.objects)
parameters$batch.threshold <- 0.01
norm.summary <- meffil.normalization.summary(norm.objects=norm.objects, pcs=pcs, parameters=parameters, verbose=T)
meffil.normalization.report(norm.summary,
                            output.file=norm.file,
                            author=author,
                            study=study)

Compare normalizations

First make sure that the raw data is the same.

raw.meffil <- meffil:::read.rg(samplesheet$Basename[1])
all(raw.meffil$R == getRed(raw.minfi)[names(raw.meffil$R),1])

Comparison of object sizes

print(object.size(qc.objects), units="Mb")
print(object.size(norm.objects), units="Mb")
print(object.size(norm.minfi), units="Mb")
print(object.size(raw.minfi), units="Mb")

Background correction

Apply meffil background correction.

probes <- meffil.probe.info("450k")
bc.meffil <- meffil:::background.correct(raw.meffil,probes)
M.meffil <- meffil:::rg.to.mu(bc.meffil,probes)$M

Apply minfi background correction.

bc.minfi <- preprocessNoob(raw.minfi, dyeCorr = FALSE)
M.minfi <- getMeth(bc.minfi)[names(M.meffil),1]

Compare results, should be identical.

quantile(M.meffil - M.minfi)

Dye bias correction

Apply meffil correction.

dye.meffil <- meffil:::dye.bias.correct(bc.meffil, probes,
                                        intensity=norm.objects[[1]]$reference.intensity)
M.meffil <- meffil:::rg.to.mu(dye.meffil,probes)$M
U.meffil <- meffil:::rg.to.mu(dye.meffil,probes)$U

Apply minfi background and dye bias correction.

dye.minfi <- preprocessNoob(raw.minfi, dyeCorr = TRUE)
M.minfi <- getMeth(dye.minfi)[names(M.meffil),1]
U.minfi <- getUnmeth(dye.minfi)[names(U.meffil),1]

Compare results, should be identical.

quantile(M.meffil - M.minfi)
quantile(U.meffil - U.minfi)
quantile(M.meffil/(M.meffil+U.meffil+100) - M.minfi/(M.minfi+U.minfi+100))

Control matrix extracted

Extract minfi control matrix.

library(matrixStats, quietly=TRUE)
control.matrix.minfi <- minfi:::.buildControlMatrix450k(minfi:::.extractFromRGSet450k(raw.minfi))

Control matrix for meffil was extracted earlier. Preprocess the matrix using the same procedure used by minfi.

control.matrix.meffil <- meffil.control.matrix(norm.objects)
control.matrix.meffil <- scale(control.matrix.meffil)
control.matrix.meffil[control.matrix.meffil > 3] <- 3
control.matrix.meffil[control.matrix.meffil < -3] <- -3
control.matrix.meffil <- scale(control.matrix.meffil)

The control variable order (columns) may be different between minfi and meffil so they are reordered.

i <- apply(control.matrix.minfi,2,function(v) {
    which.min(apply(control.matrix.meffil,2,function(w) {
        sum(abs(v-w))
    }))
})
control.matrix.meffil <- control.matrix.meffil[,i]

Compare resulting matrices, should be identical.

quantile(control.matrix.meffil - control.matrix.minfi)

Final beta values

beta.minfi <- getBeta(norm.minfi)
quantile(beta.meffil - beta.minfi[rownames(beta.meffil),])

On what chromosomes are the biggest differences appearing?

features <- meffil.get.features("450k")
sites <- features[which(features$target == "methylation"),]
site.chromosome <- sites$chr[match(rownames(beta.meffil), sites$name)]
sex <- sapply(norm.objects, function(object) object$predicted.sex)
is.diff <- abs(beta.meffil - beta.minfi[rownames(beta.meffil),]) > 1e-4
table(site.chromosome[which(is.diff, arr.ind=T)[,"row"]])
table(site.chromosome[which(is.diff[,which(sex=="M")], arr.ind=T)[,"row"]])
table(site.chromosome[which(is.diff[,which(sex=="F")], arr.ind=T)[,"row"]])

These results are not surprising because chromosome Y not handled like minfi in either males or females, and chromosome X is handled just like minfi in females but not in males.

autosomal.sites <- intersect(meffil.get.autosomal.sites("450k"), rownames(beta.meffil))
quantile(beta.meffil[autosomal.sites,] - beta.minfi[autosomal.sites,],na.rm=T)

These are small differences relative to differences with unormalized data:

beta.raw <- getBeta(raw.minfi)
quantile(beta.meffil[autosomal.sites,] - beta.raw[autosomal.sites,], na.rm=T)
quantile(beta.minfi[autosomal.sites,] - beta.raw[autosomal.sites,], na.rm=T)

In spite of the differences, CG correlations between methods are pretty close to 1.

male.idx <- which(rowSums(is.diff[,sex=="M"]) >= 5)
male.cg.r <- unlist(mclapply(male.idx, function(idx) {
    cor(beta.meffil[idx, sex=="M"], beta.minfi[rownames(beta.meffil)[idx], sex=="M"])
}))
quantile(male.cg.r, probs=c(0.05,0.1,0.25,0.5))

female.idx <- which(rowSums(is.diff[,sex=="F"]) > 0)
female.cg.r <- sapply(female.idx[1:200], function(idx) {
    cor(beta.meffil[idx, sex=="F"], beta.minfi[rownames(beta.meffil)[idx], sex=="F"])
})
quantile(female.cg.r, probs=c(0.05,0.1,0.25, 0.5))

Sex

sex.minfi <- as.data.frame(getSex(norm.minfi))
names(norm.objects) <- sapply(norm.objects, function(obj) basename(obj$basename))
sex.meffil <- data.frame(predicted=sapply(norm.objects, function(obj) obj$predicted.sex),
                         x.signal=sapply(norm.objects, function(obj) obj$x.signal),
                         y.signal=sapply(norm.objects, function(obj) obj$y.signal))
all(sex.minfi[rownames(sex.meffil),"predictedSex"] == sex.meffil$predicted)
quantile(sex.minfi[rownames(sex.meffil),"xMed"] - sex.meffil$x.signal)
quantile(sex.minfi[rownames(sex.meffil),"yMed"] - sex.meffil$y.signal)

Cell count estimates

counts.meffil <- t(meffil.cell.count.estimates(norm.objects))

require("FlowSorted.Blood.450k")
counts.minfi <- estimateCellCounts(raw.minfi)
counts.minfi <- counts.minfi[rownames(counts.meffil), colnames(counts.meffil)]

sapply(rownames(counts.meffil), function(i) cor(counts.meffil[i,], counts.minfi[i,]))
sapply(colnames(counts.meffil), function(i) cor(counts.meffil[,i], counts.minfi[,i]))

for (cell.type in colnames(counts.meffil)) {
    lim <- range(c(counts.meffil[,cell.type], counts.minfi[,cell.type]))
    plot(counts.meffil[,cell.type], counts.minfi[,cell.type], pch=19,
         main=cell.type,
         sub=paste("R =", format(cor(counts.meffil[,cell.type], counts.minfi[,cell.type]), digits=3)),
         xlim=lim,ylim=lim)
    abline(a=0,b=1,col="red")
}

counts.beta <- meffil.estimate.cell.counts.from.betas(beta.meffil, cell.type.reference)

for (cell.type in colnames(counts.meffil)) {
    lim <- range(c(counts.meffil[,cell.type], counts.beta[,cell.type]))
    plot(counts.meffil[,cell.type], counts.beta[,cell.type], pch=19,
         main=cell.type,
         sub=paste("R =", format(cor(counts.meffil[,cell.type], counts.beta[,cell.type]), digits=3)),
         xlim=lim,ylim=lim)
    abline(a=0,b=1,col="red")
}

SNP signals

snps.meffil <- meffil.snp.betas(qc.objects)
snps.minfi <- getSnpBeta(raw.minfi)
quantile(snps.meffil - snps.minfi[rownames(snps.meffil),])

perishky/meffil documentation built on Feb. 1, 2025, 6:53 p.m.