Hi there,

I am really a green hand in methylation but I have to practise some methylation analysis recently. I know that the differential expression gene analysis can depend on some R packages like {limma}, {edgeR}, {DESeq} easily based on gene expression only, but I do not know if methylation data could be analyzed in a similar way based on beta value to find methylated probes between two or more groups.

The TCGAbiolinks package contains Differentially methylated regions Analysis based on function TCGAanalyze_DMR, but it requires a SummarizedExperiment object which include some chromosomal information. The following is an example described in TCGAbiolinks:

library(TCGAbiolinks)
nrows <- 200; ncols <- 20
counts <- matrix(runif(nrows * ncols, 0, 1), nrows)
rowRanges <- GenomicRanges::GRanges(rep(c("chr1", "chr2"), c(50, 150)),
                                    IRanges::IRanges(floor(runif(200, 1e5, 1e6)), width=100),
                                    strand=sample(c("+", "-"), 200, TRUE),
                                    feature_id=sprintf("ID%03d", 1:200))
colData <- S4Vectors::DataFrame(Treatment=rep(c("ChIP", "Input"), 5),
                                row.names=LETTERS[1:20],
                                group=rep(c("group1","group2"),c(10,10)))
data <- SummarizedExperiment::SummarizedExperiment(
  assays=S4Vectors::SimpleList(counts=counts),
  rowRanges=rowRanges,
  colData=colData)
SummarizedExperiment::colData(data)$group <- c(rep("group 1",ncol(data)/2),
                                               rep("group 2",ncol(data)/2))
hypo.hyper <- TCGAanalyze_DMR(data, p.cut = 0.85,"group","group 1","group 2")

and you will get output like this:

feature_id  mean.group.1    mean.group.2    diffmean.group.1.group.2    p.value.group.1.group.2 p.value.adj.group.1.group.2 status.group.1.group.2  diffmean.group.2.group.1    p.value.group.2.group.1 p.value.adj.group.2.group.1 status.group.2.group.1
cg13332474  0.119938    0.15153 0.031592    0.651579082 0.973307504 Not Significant -0.031592   0.651579082 0.973307504 Not Significant
cg00651829  0.143226    0.146714    0.003488    0.777432789 0.982499101 Not Significant -0.003488   0.777432789 0.982499101 Not Significant

Thus,is this chromosomal information like start/end/strand necessary for probe differential methylation analysis and what is the common workflow for differential methylated probe analysis?

I would be greatly appreciated if someone could give me a hint.