I am working with a dataset of quantitative targeted LC-MS/MS-measured metabolites. I am advised to perform following normalization steps:
- Removal of metabolites where >20% of samples are below LOD (there are no missing values)
- Merging of both batches via ComBat
- Quantile normalization
Moreover for multivariate analysis I should use pareto scaling. From my understanding all these steps (except 1.) are some kinds of normalization methods. So my questions are:
- Isn't this too much and/or partly redundant?
- If it is not per se too much, does it make sense in the first place?
- Does the suggested order of these steps make sense?
I do know all these methods and that individually they make sense. However, I couldn't find much about the combination of these. I also tried to use the NOREVA tool but I couldn't get it to work while I tried many different browsers on different OSs. The code for NOREVA (which is a collection of tests to evaluate the effect fo different normalization strategies) is not available and it would probably take me a long time to implement all these tools by myself. So I first wanted to ensure if anyone has any insights on the proposed normalization strategy before choosing some criteria to evaluate it.
Thanks in advance!