Dear mixOmics community
The latest month I’v been working on data integration of several metabolomics/lipidomics datasets with microbiome (16S) data. Lately I encoutered this strange behaviour of the block.splsda function. When I create a model using this list.keepX object:
 17 6
 14 5
 20 12
 6 5
The list indicates my otu (microbiome) dataset should have 6 & 5 variables for comp 1 & 2 respectivly in the model. However, when looking at the resulting model we can see that there are 51 & 5 variables for the otu dataset! In another try, with the same input, it resulted in 52 & 6 variables!
Any idea on why this might be the case, or what is happening here?
While I’m at it, I have some other (not as important) questions:
For all my datasets, both in multi omic & single omic analyses, the optimal number of components is always = 1. Is this erratic behaviour, or should this be no problem? For visualization purposes I always construct models with ncomp = 2. Also the error rate of my models stays high, even after tuning, so I guess my data is not suited for prediction, but can be used for pathway analysis / search for biologically relevant correlations?
Is there a way to disregard “within block correlations” when plotting the circosplot or networks?
Many thanks in advance if someone takes the time to read and answer this!
PhD Student @ Laboratory for Chemical Analysis, Faculty of Veterinary Medicine, Ghent University