Question: Is right normalize metabolomics "counts" by Variance Stabilized Transformation using VST DESeq2 package function?
gravatar for panconchoclo
5 weeks ago by
panconchoclo10 wrote:

I want to normalize metabolomics 'counts' data by Variance Stabilizing Transformation. In the meantime I already did that by VST function of using DESeq package and this improve in my opinion the clustering between replicates on a PCA plot which make sense for me. However I just wonder if I need actually use some modification on the function to avoid include library size normalization and what is different regarding the old VSN function of Limma package. Maybe I should be use that instead the implemented VST on DESeq? I mean; Is DESEq using a wrapper of VSN Limma for permom the VST or doing something else to normalize by library size? Any comment regarding my approach will be appreciated guys,

vst deseq R metabolomics • 171 views
ADD COMMENTlink modified 2 days ago by Biostar ♦♦ 20 • written 5 weeks ago by panconchoclo10

Is there any reason why you decided to use the variance stabilising transformation? From my experience, most log2 transform the peak areas (after QC), followed by a further Z-scale transformation. Admittedly, this is not ideal. Metabolomics suffers from a lack of reproducibility, and exhibits high variability, as you are probably aware.

ADD REPLYlink written 5 weeks ago by Kevin Blighe41k

I would post this over at BioC to get a response from Mike Love (the DESeq2 maintainer/developer). Don't forget to post a couple of plots to show how your data are distributed and give some background information on the nature of these data, e.g. if they follow a certain distribution and how replicates compare to each other.

ADD REPLYlink modified 5 weeks ago • written 5 weeks ago by ATpoint15k

Hi guys thank you for your reply.

Kevin I decided by VST because there are Heteroscedasticity. The behaviour of Means vs SD after and before VST transformation in my data is showed.

In the plot 1 you will see that there are some metabolites with a lot of variance. After VST the differences are slower (see scales of Y axis)

before VST normalization(raw data): Image and video hosting by TinyPic

after VST:

Image and video hosting by TinyPic

If I do the PCA of raw data my replicates are not all of them together. If I do VST before PCA the behavioir in this sense is better. I mean my replicates are closer between them.

ADD REPLYlink modified 5 weeks ago • written 5 weeks ago by panconchoclo10

I would go with the suggestion of ATpoint and ask this on the Bioconductor forum. Michael Love will provide an answer or comment.

ADD REPLYlink written 5 weeks ago by Kevin Blighe41k

Thank you Kevin I will do that. To avoid repeat exactly the same question there I will dig more in detail on these packages.

ADD REPLYlink written 5 weeks ago by panconchoclo10
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