Question: differential methylated probe analysis
0
gravatar for sugus
21 months ago by
sugus40
China Pharmaceutical University
sugus40 wrote:

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.

ADD COMMENTlink written 21 months ago by sugus40

Hi, as far a I know, the genomic position is not necessary, unless TCGAbiolinks is doing some further analysis or adjustment whereby probes in close proximity will be taken into account. Also, as far as I know, TCGAbiolinks is just performing a simple Wilcoxon SIgned Rank Test on each probe, and also comparing the mean (β) across the 2 groups.

You can implement a Wilcoxon test in R, and also comparing the mean (simply subtract the mean in both groups).

Kevin

ADD REPLYlink written 21 months ago by Kevin Blighe47k

Thank you so much and I have done relative test based on wilcox to perform differential methylation probes.

ADD REPLYlink written 21 months ago by sugus40

Great - then you do not have to worry about genomic position.

Good luck

Kevin

ADD REPLYlink written 21 months ago by Kevin Blighe47k
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