differential methylated probe analysis
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Entering edit mode
3.4 years ago
sugus ▴ 60

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.

R methylation differential methylated beta value • 1.6k views
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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

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

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

Good luck

Kevin