'Multi-omics', like 'systems biology', 'AI', and 'machine learning', has become a sort of 'buzz' term. People hear these terms and get excited, without giving much thought about what they mean. I am not stating that you are doing this in this situation... just setting the scene.
In reality, multi-omic techniques have been around for a long time. Probably the best known is eQTL, whereby gene expression data is essentially regressed with genetic variant (GWAS) data in order to gauge the effect of different variants on the expression of nearby genes.
In your case, you want to use iClusterPlus, Where have you looked for how to use this? There are quite a few use cases in the manual: iClusterPlus: integrative clustering of multiple genomic data sets. If your data is not yet in the correct format, then, my apologies, it is your role to get it [your data] into the correct format. There does not have to be a tutorial for everything.
You should also have a definitive hypothesis (or hypotheses) that you want to test. For example, why do you want to integrate these data? - just for fun or training? People can produce nice looking heatmaps and network plots of data that has been integrated, but most, from what I have seen, are meaningless when taking a clinical perspective. Fair enough if it is entirely an exploratory analysis, though.
I would encourage you to look at the TCGA consortium's published work on endometrial cancer, where they provide for an excellent example of 'intelligent' multi-omics. They essentially defined new sub-types of endometrial cancer based on copy number profiles, and then found that each sub-type also had distinct somatic mutations and methylation profiles. They did not produce any fancy graphs or heatmaps that mean nothing - they just went about the process in an intellectual fashion.