Table 1.

Categories of Multi-Omics Data Integration^*

Type	Advantages	Limitations	Example method
Early Integration	Can identify a great number of independent signals	Imbalanced when numbers of variables or data distributions are dissimilar across type of omics data	LUCID [154]
Mixed	Removes heterogeneity between datasets to facilitate analysis with a variety of methods	Most methods assume equal importance of each type of omics data	OmicsNet [155, 156]
Intermediate	Produces omics-specific and multi-omics outputs	Pre-processing and feature selection necessary first to prevent issues from data heterogeneity	JIVE [157]
Late Integration	Picks up related signals from each omics layer	Does not consider interactions between omics	MOGONET [158]
Hierarchical	Incorporates knowledge on regulatory relationships between omics into the integration	Methods designed to fit omics with specific relationships (i.e. epigenetics with gene expression)	iBAG [159]

^*Categorizations as first introduced by Picard et al. 2021 [150].