## ----, echo=FALSE--------------------------------------------------------
knitr::opts_chunk$set(cache=TRUE)

## ----, echo=FALSE--------------------------------------------------------
suppressPackageStartupMessages({
    require(minfi)
    require(minfiData)
})

## ----, eval=FALSE--------------------------------------------------------
#  require(minfi)
#  require(minfiData)
#  browseVignettes("minfi")

## ------------------------------------------------------------------------
baseDir <- system.file("extdata", package = "minfiData")
baseDir
dir(baseDir)
dir(file.path(baseDir, "5723646052"))

## ------------------------------------------------------------------------
## 'pData'
targets <- read.450k.sheet(baseDir)
head(targets)
## 'raw' probe-level data
RGset <- read.450k.exp(base = baseDir, targets = targets)

## ------------------------------------------------------------------------
## Basic QA -- comparable densities across samples?
densityPlot(RGset, sampGroups = RGset$Sample_Group, 
    main = "Beta", xlab = "Beta")

## ------------------------------------------------------------------------
## background correction and normalization
##     like Illumina Genome Studio (other approaches available)
MSet.norm <- preprocessIllumina(RGset, bg.correct = TRUE,
    normalize = "controls", reference = 2)

## ------------------------------------------------------------------------
## How similar are the samples to one another?
mdsPlot(MSet.norm, numPositions = 1000, sampGroups = MSet.norm$Sample_Group, 
    sampNames = MSet.norm$Sample_Name)

## ------------------------------------------------------------------------
## Identify probes with methylation status differing between groups
mset <- MSet.norm[1:100000,]
## logit(beta)
M <- getM(mset, type = "beta", betaThreshold = 0.001)
dmp <- dmpFinder(M, pheno=mset$Sample_Group, type="categorical")
head(dmp)

## ------------------------------------------------------------------------
plotCpg(mset, cpg=rownames(dmp)[1], pheno=mset$Sample_Group)

## ------------------------------------------------------------------------
## Genomic locations
mset <- mset[rownames(dmp),]
mse <- mapToGenome(mset)              # 'SummarizedExperiment'
rowData(mse)
mcols(rowData(mse)) <- cbind(mcols(rowData(mse)), dmp)