More Is Better: Recent Progress in Multi-Omics Data Integration Methods

Overview

Journal Front Genet

Date 2017 Jul 4

PMID 28670325

Citations 289

Authors

Sijia Huang

Kumardeep Chaudhary

Lana X Garmire

Affiliations

Soon will be listed here.

Abstract

Multi-omics data integration is one of the major challenges in the era of precision medicine. Considerable work has been done with the advent of high-throughput studies, which have enabled the data access for downstream analyses. To improve the clinical outcome prediction, a gamut of software tools has been developed. This review outlines the progress done in the field of multi-omics integration and comprehensive tools developed so far in this field. Further, we discuss the integration methods to predict patient survival at the end of the review.

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References

You Z, Yin Z, Han K, Huang D, Zhou X . A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network. BMC Bioinformatics. 2010; 11:343. PMC: 2909217. DOI: 10.1186/1471-2105-11-343. View

Lock E, Hoadley K, Marron J, Nobel A . JOINT AND INDIVIDUAL VARIATION EXPLAINED (JIVE) FOR INTEGRATED ANALYSIS OF MULTIPLE DATA TYPES. Ann Appl Stat. 2013; 7(1):523-542. PMC: 3671601. DOI: 10.1214/12-AOAS597. View

Lin D, Zhang J, Li J, Calhoun V, Deng H, Wang Y . Group sparse canonical correlation analysis for genomic data integration. BMC Bioinformatics. 2013; 14:245. PMC: 3751310. DOI: 10.1186/1471-2105-14-245. View

Chen J, Bushman F, Lewis J, Wu G, Li H . Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis. Biostatistics. 2012; 14(2):244-58. PMC: 3590923. DOI: 10.1093/biostatistics/kxs038. View

Rabin B, Gaglio B, Sanders T, Nekhlyudov L, Dearing J, Bull S . Predicting cancer prognosis using interactive online tools: a systematic review and implications for cancer care providers. Cancer Epidemiol Biomarkers Prev. 2013; 22(10):1645-56. PMC: 3832108. DOI: 10.1158/1055-9965.EPI-13-0513. View

Cline M, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C . Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007; 2(10):2366-82. PMC: 3685583. DOI: 10.1038/nprot.2007.324. View

Bonnet E, Calzone L, Michoel T . Integrative multi-omics module network inference with Lemon-Tree. PLoS Comput Biol. 2015; 11(2):e1003983. PMC: 4332478. DOI: 10.1371/journal.pcbi.1003983. View

Le Cao K, Martin P, Robert-Granie C, Besse P . Sparse canonical methods for biological data integration: application to a cross-platform study. BMC Bioinformatics. 2009; 10:34. PMC: 2640358. DOI: 10.1186/1471-2105-10-34. View

Ideker T, Ozier O, Schwikowski B, Siegel A . Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics. 2002; 18 Suppl 1:S233-40. DOI: 10.1093/bioinformatics/18.suppl_1.s233. View

10.

Artez A, Bernabe R, Eerola I, Hemsley F, Jennings J, Kerr D . International network of cancer genome projects. Nature. 2010; 464(7291):993-8. PMC: 2902243. DOI: 10.1038/nature08987. View

11.

Shen R, Mo Q, Schultz N, Seshan V, Olshen A, Huse J . Integrative subtype discovery in glioblastoma using iCluster. PLoS One. 2012; 7(4):e35236. PMC: 3335101. DOI: 10.1371/journal.pone.0035236. View

12.

Huang S, Yee C, Ching T, Yu H, Garmire L . A novel model to combine clinical and pathway-based transcriptomic information for the prognosis prediction of breast cancer. PLoS Comput Biol. 2014; 10(9):e1003851. PMC: 4168973. DOI: 10.1371/journal.pcbi.1003851. View

13.

Parkhomenko E, Tritchler D, Beyene J . Sparse canonical correlation analysis with application to genomic data integration. Stat Appl Genet Mol Biol. 2009; 8:Article 1. DOI: 10.2202/1544-6115.1406. View

14.

J van t Veer L, Dai H, van de Vijver M, He Y, Hart A, Mao M . Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002; 415(6871):530-6. DOI: 10.1038/415530a. View

15.

Mankoo P, Shen R, Schultz N, Levine D, Sander C . Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles. PLoS One. 2011; 6(11):e24709. PMC: 3207809. DOI: 10.1371/journal.pone.0024709. View

16.

Zhang S, Li Q, Liu J, Zhou X . A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics. 2011; 27(13):i401-9. PMC: 3117336. DOI: 10.1093/bioinformatics/btr206. View

17.

Zhang S, Liu C, Li W, Shen H, Laird P, Zhou X . Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Res. 2012; 40(19):9379-91. PMC: 3479191. DOI: 10.1093/nar/gks725. View

18.

Li W, Zhang S, Liu C, Zhou X . Identifying multi-layer gene regulatory modules from multi-dimensional genomic data. Bioinformatics. 2012; 28(19):2458-66. PMC: 3463121. DOI: 10.1093/bioinformatics/bts476. View

19.

Speicher N, Pfeifer N . Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery. Bioinformatics. 2015; 31(12):i268-75. PMC: 4765854. DOI: 10.1093/bioinformatics/btv244. View

20.

Chen J, Zhang S . Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data. Bioinformatics. 2016; 32(11):1724-32. DOI: 10.1093/bioinformatics/btw059. View