Table 1 Details of the four main statistical integration approaches and suggested tools

Correlation-based integration

Seeks to identify correlative links between elements of different -omics datasets

3Omics

Kuo et al. (2013)

Concatenation-based integration

Groups different -omics or single-omic dataset from different experiments or experimental conditions into a single matrix and then performs integrated analysis

Multiple co-inertia analysis (MCIA)

Meng et al. (2014)

Joint and Individual Variation Explained (JIVE)

Lock et al. (2013)

Multivariate-based integration

Relies on multivariate statistical methods such as canonical correlation analysis (CCA) (Jozefczuk et al. 2010) and orthogonal partial least squares discriminant analysis (OPLS-DA) (Boccard and Rutledge 2013) to calculate relationship between different levels of -omics data

R package mixOmics

González et al. (2011)

Pathway-based integration

Integrates different -omics levels by relying on existing biological knowledge gathered from metabolic pathways such as Kegg and wikipathways (Kutmon et al. 2015)

InCroMAP and IMPALA for integrated pathway-based analysis

Eichner et al. (2014), Kamburov et al. (2011)

SAMNetWeb to generate biological networks with transcriptomics and proteomics data

Gosline et al. (2015)

MetScape cytoscape plugin to produce metabolic networks from transcriptomics and metabolite data

Karnovsky et al. (2012)