. Author manuscript; available in PMC: 2014 May 1.

Published in final edited form as: Proc Nutr Soc. 2013 Feb 6;72(2):207–218. doi: 10.1017/S0029665113000025

Table 3.

Summary of methods for integrative analysis of multiple omics datasets.

Integration Approach	Reference	Methodology/Tools	Omics Data
1) Concordance Analysis
	Hirai et al⁽³⁵⁾, 2004	PCA, SOM	Transcriptome and metabolome in Arabidopsis
	Hirai et al⁽³⁶⁾, 2005	network analysis	Transcriptome and metabolome in Arabidopsis
	Le Cao et al⁽³⁷⁾, 2009	sparse PLS	cDNA and mRNA in NCI60 cancer cell lines
	Van Deun et al⁽³⁹⁾, 2009	multiple methods	Comparative analysis of integration methods assuming data on the same subjects
2) Sequential Integration
	Putluri et al⁽⁴⁰⁾, 2011	DE, OCM	Metabolomics, meta-genomics in Prostate cancer
	Putluri et al⁽⁴¹⁾, 2011	DE, OCM, CA, PLS	Metabolomics abundance & flux data, meta-genomics in Bladder cancer
	Imielinski et al⁽⁴²⁾, 2012	GSEA, network analysis	Transcriptomics, proteomics in Breast cancer
3) Concurrent Integration
	Poisson et al⁽⁴³⁾, 2011	DE, p-value weighting, GSEA	Transcriptomics, metabolomics
	Jauhiainen et al⁽⁴⁴⁾, 2012	sparse mixed linear model	Transcriptomics and metabolomics in cancer
	Shojaie A, Panzitt K, Putluri N, Putluri V, Samanta S, Vareed SK, Basu S, Ittmann M, Michailidis G, Palpattu G, Sreekumar A (2012) A Network-Based Integrative Approach to Study the Role of Metabolic Pathways in Prostate Cancer Progression	NetGSA, GSEA, rank- based integration	Transcriptomics and metabolomics in Prostate cancer

Abbreviations: DE, differential analysis; GSEA, gene set enrichment analysis; CA, correlation analysis; PCA, principal component analysis; SOM, selforganizing maps; PLS, partial least squares; OCM Oncomine concept mapping; NetGSA, network-based gene set analysis.