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 multi-omics data and plots to show outputs.
I’m especially interested in unsupervised N-integration, 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 2-integrations (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 N-integration (block.plsda) but not for unsupervised N-integration (block.pls). Here are my questions:
The 2-integration’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?