Dear mixOmics members,
First of all, I would like to thank the mixOmics team for their amazing work!
I’m a new user of this tool and I really appreciate working with it as it is easy to use, and it contains many reduction dimension methods to integrate multiomics data and plots to show outputs.
I’m especially interested in unsupervised Nintegration, which is done with the method block.pls. My goal is to find correlations (or links) between my variables coming from different omics data. I started with 2integrations (rcc and canonical pls) and used the CIM plot to show correlations between my variables. I have seen that another CIM version was available for supervised Nintegration (block.plsda) but not for unsupervised Nintegration (block.pls). Here are my questions:

The 2integration’s CIM presents variables from the first omics dataset against variables from the second. Hence it cannot be generalized to integration of 3 or more omics dataset. However, I would like to know if it is possible to use the computational idea behind CIM with more than 2 omics dataset. Indeed, to create the CIM, mixOmics creates a similarity matrix then uses a hierarchical clustering on it. It gives values used to create the CIM. Can I make a similarity matrix with 3 or more omics dataset then use a hierarchical clustering on it?

CIM for blockplsda is a more “classic” heatmap as its rows and columns are those from the X input data. This plot can also be used to find correlations between omics data, but until now it can only be used with supervised learning. Has anyone thought about creating a CIM version for blockpls?
Best regards,
Emile