Dear Xia Lab team,
I am working on a study investigating the relationship between fruit quality traits and metabolomic profiles in Malus sieversii (wild apple). I have two sets of metabolomic data:
- GC-MS data (~500 volatile metabolites)
- Widely-targeted metabolomics data (~2000 non-volatile metabolites)
My goal is to quantify the relative contributions of these two datasets to the variation of six fruit quality traits (fruit weight, length, diameter, firmness, SSC, TA) using Variance Partitioning Analysis (VPA).
Specifically, I need to decompose the total variance into:
- Unique contribution from GC-MS
- Unique contribution from widely-targeted metabolome
- Shared contribution
- Unexplained residual
My questions:
- Does MetaboAnalyst have a built-in VPA module? If so, what is the workflow? If not, which module and which tools within it can be used to process the data to achieve this, and what is the workflow?
- Since I have ~60 samples but 2500 metabolites, should I perform dimensionality reduction before VPA?
- How should I interpret negative values in shared contributions?
I have attached a sample of my data format. Any guidance would be greatly appreciated.
Thank you!