Question: Design matrix for voom: with or without batch blocking?
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gravatar for hesse.sebastian
5 months ago by
hesse.sebastian80 wrote:

To calculate differential expression in a proteomics dataset I would like to use LIMMA. I have two questions about it.

1) So far I did the LIMMA analysis on my log2 transformed data only, however it appears that it is recommended to perform a room transformation first as they represent counted data (from MS/MS DIA).

For the voom transformation a design matrix is requested, apparently the same one as for LIMMA. Due to batch effects I am blocking for two batches in the design matrix for LIMMA, using: design <- model.matrix(~0 + Causative.gene +batch_date.proc + batch_cell.number, data = pData(ExSet)).

Should I use the same matrix vor VOOM or better not include the batch block in it?

2) Could you tell me a code how I could check if VOOM transformation for my data is actually necessary? As much as I understand VOOM corrects for heteroskedacity in the data. How could I check if my expression data are actually affected by heteroskedacity? (I found this post about it but don't know how to apply this to my data with 170 samples and 8 groups to compare: https://datascienceplus.com/how-to-detect-heteroscedasticity-and-rectify-it/)

Thanks a lot! Sebastian

voom limma R proteomics • 199 views
ADD COMMENTlink modified 5 months ago by i.sudbery4.8k • written 5 months ago by hesse.sebastian80
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gravatar for i.sudbery
5 months ago by
i.sudbery4.8k
Sheffield, UK
i.sudbery4.8k wrote:

If your data is count data, then it will definitely suffer from heteroskedacity. There are other ways to make count data suitable for Limma, e.g. limma-trend, but AFAIK voom is the most robust. One way to see this is to plot variance against mean expression for each protein. You will see that genes with higher expression have a higher variance.

I would use the same design matrix as you will use in limma, including the blocking factors.

ADD COMMENTlink written 5 months ago by i.sudbery4.8k
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