I have a matrix of Beta values with CpGs (18500 CpGs) as columns and samples as rows called methyl. I am making a GenomicRatioSet using minfi before using it on the bumphunter function applying the code below:
GRS <- makeGenomicRatioSetFromMatrix(t(methyl), what = "Beta")
Then I am also turning Beta values to M values and storing in this object using the code:
assays(GRS)[["M"]] <- getM(GRS)
However, when I run bumphunter with same parameters on M values and Beta values like this:
with_beta <- bumphunter(object = GRS, design = mod, cutoff=0.3, B=0 ,type = "Beta") with_M <- bumphunter(object = GRS, design = mod, cutoff=0.3, B=0 ,type = "M")
Turning Beta to M values introduces some NaNs but bumphunter founds and removes them. The problem is with beta values it founds 57 bumps and with M values it founds 10659 bumps even though it removed some values. Is it normal to get this much of a difference between Beta and M values? What am I doing wrong?
