Skewed beta-distribution from Methylation EPIC array data
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@erwintomasich-22794
Last seen 16 months ago

Dear all, I'm a rookie in the field of bioinformatics. We recently started to work with Methylation EPIC array data from Illumina.

For our last 4 chips we received weird (skewed) beta distribution densities even after within-array normalization using the ChAMP package. Has anyone ever seen something similiar and have an advice?

Your help is very much appreciated!

Kind regards, Erwin

enter image description here

ChAMP EPIC Illumina Methylation • 192 views
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Hi Erwin:

I have not met this situation before, but I have an idea about this situation.

Firstly I suspect the quality of the data is not very good, even before normalization it should not be like that.

Secondly, BMIQ method (I assume you have used that) is designed for beta distribution data, so if the original data is even not beta distributed, maybe it would not working (even fail).

Thirdly, BMIQ is done by separating beta distribution into 3 parts, lower-value(for example, 0-0.4), middle-value (0.4-0.7), higher value(0.7-1)... And fit each part for normalization. So in your data, I would suspect BMIQ encountered some problem in the middle and high-value area, for example from beta 0.4 to 1. Thus the lower part looks right, but the rest 2 looks kind of wired. In other words, possibly one type of probe is not very good in the middle-high area. I think maybe you can try check it using QC.GUI() function, on origin matrix, like below:

QC.GUI(myLoad$beta,myLoad$pd$Sample_Group,arraytype="EPIC")

In the second panel, you may see the distirbution of two difference types.

So I think maybe you can try other normalization methods, see if the plot is still like this. I think some other methods, like PBC maybe not be that picky for data distribution.

Again, I have to say I never encounter this situation, but I am just saying some possibilities I think might cause this.

Best Tian

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Dear Tian,

Thanks for your reply and sharing your experience and expertise. You were right with BMIQ and its failure!

It's still strange because it has worked with data from a parallel processed project. We will try to figure out what might have caused a loss in quality...

Best, Erwin

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