Version Compatibility

Reference examples tested with: mixOmics 6.26+.

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('mixOmics') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

The DIABLO model is block.splsda - there is no function literally named diablo. mixOmics input is samples-by-features (the opposite of MOFA2's features-by-samples), and scale=TRUE is the default, so feed appropriately transformed but not pre-standardized matrices.

mixOmics Multi-Omics Analysis

"Find a cross-omic signature that discriminates my groups" -> Project the blocks onto sparse latent components that maximize an association criterion - because these methods maximize covariance, not truth, so a signature that discriminates the training cohort is guaranteed and only out-of-sample replication makes it real.

• R: block.splsda() (DIABLO, supervised), spls() (pairwise), mint.splsda() (multi-study)

Scope: supervised multi-block discriminant integration (DIABLO), sparse pairwise correlation (sPLS), regularized CCA (rCCA), and multi-study integration (MINT). Unsupervised factor models -> mofa-integration. The method-selection decision -> integration-design. Generic cross-validation / leakage theory -> machine-learning/model-validation. Per-omic scaling/batch -> data-harmonization. Enrichment of selected features -> pathway-analysis/go-enrichment.