I am writing my first post because I have just started to use the rCCA method to integrate two datasets, i.e. DEGs and MixMC-filtered 16S data. Previous to rCCA, I made several trials with sPLS, but I could not get any valid model out of my data.
Now, rCCA seems to be working, but I have some doubts about the reliability of what I get. When you use other methods provided by Mixomics, like sPLS, you use a threshold to decide whether your Q2 values can be accepted or not.
But with rCCA, I do not see an equivalent metric that could be used as a guidance. Of course, one could use scree plots and see if the R2 values obtained are high ‘enough’ to be considered significant. In my case, the first three components are roughly in the 0.6 - 0.8 range.
I also have another question. I get ‘good’ results, i.e. the ones I have just mentioned, when I use the ‘shrinking’ method, but when I use the ‘CV’ one the R2 values decrease dramatically. Does this mean I have to go for the ‘shrinkage’ method, or is there some bias to consider?
Thanks in advance for your help. Best regards,