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)

Normalizing dataset GSE86831

Download example data set

acc <- "GSE86831"
url <- "ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE86nnn/GSE86831/suppl/GSE86831_RAW.tar"
dir.create(path <- acc)    
if (length(list.files(path, "*.idat$")) == 0) {
    filename <-  file.path(path, basename(url))
    download.file(url, filename)
    cat(date(), "Extracting files from GEO archive.\n")
    system(paste("cd", path, ";", "tar xvf", basename(filename)))
    unlink(filename)
    cat(date(), "Unzipping IDAT files.\n")
    system(paste("cd", path, ";", "gunzip *.idat.gz"))

    library(GEOquery)
    geo <- getGEO(acc, GSEMatrix=F)
    geo <- lapply(geo@gsms, function(gsm) unlist(gsm@header))
    geo <- do.call(rbind, geo)
    geo <- as.data.frame(geo, stringAsFactors=F)
    write.csv(geo, file=file.path(path, "samples.csv"))
}

Normalize dataset

Create samplesheet

library(meffil)

samplesheet <- meffil.create.samplesheet(path)

options(mc.cores=3)

Parameters.

doc.path <- paste(acc, "demo", sep="-")
qc.file <- file.path(doc.path, "qc-report.html")
author <- "Prickett, et al."
study <- "Silver-Russell syndrome patients (GEO:GSE55491)"
norm.file <- file.path(doc.path, "normalization-report.html")
cell.type.reference <- "blood gse35069"

Generate quality control objects.

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

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 low quality samples.

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, n.cross=3)$plot)

Normalize dataset.

number.pcs <- 2
norm.objects <- meffil.normalize.quantiles(qc.objects, number.pcs=number.pcs, verbose=T)
norm.dataset <- meffil.normalize.samples(norm.objects,
                                        just.beta=F, 
                                        cpglist.remove=qc.summary$bad.cpgs$name,
                                        verbose=T)

Generate normalization report.

beta <- meffil.get.beta(norm.dataset$M, norm.dataset$U)
pcs <- meffil.methylation.pcs(beta, sites=meffil.get.autosomal.sites("450k"), verbose=T)

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)