Table 3.

Summary of the major multi-omics integration approaches

Integration method	Analysis method	Characteristics	Elements	Reference
Statistical-based	Correlation	Simplicity and intuitiveness	Pearson, Spearman	[100]
	Clustering using data set connection	Distinguish clear and unique groups	Hierarchical, K-means, random forests	[101]
		Highly dependent on the size between data sets
	Multivariate	Powerfully applied in a metadata analysis	PCA, PLS	[102]
		Predict various aspects or trends of a data set
Function-based	Reference database	Complex connections between various types of molecular elements	KEGG, GO, Reactome	[103]
		Differences exist in different species
	Networking	Provides critical clusters, modules, and hubs	GCN, WGCNA	[104]
		Complex connections between various types of molecular elements

PCA, principal component analysis; PLS, partial least squares; KEGG, Kyoto encyclopedia of genes and genomes; GO, gene ontology; GCN, gene co-expression network; WGCNA, weighted gene co-expression network analysis.