Similarity Network Fusion for Aggregating Data Types on a Genomic Scale

Overview

Journal Nat Methods

Specialties Biomedical Engineering
Pathology

Date 2014 Jan 28

PMID 24464287

Citations 686

Authors

Bo Wang

Aziz M Mezlini

Feyyaz Demir

Marc Fiume

Zhuowen Tu

Michael Brudno

Benjamin Haibe-Kains

Anna Goldenberg

Affiliations

Soon will be listed here.

Abstract

Recent technologies have made it cost-effective to collect diverse types of genome-wide data. Computational methods are needed to combine these data to create a comprehensive view of a given disease or a biological process. Similarity network fusion (SNF) solves this problem by constructing networks of samples (e.g., patients) for each available data type and then efficiently fusing these into one network that represents the full spectrum of underlying data. For example, to create a comprehensive view of a disease given a cohort of patients, SNF computes and fuses patient similarity networks obtained from each of their data types separately, taking advantage of the complementarity in the data. We used SNF to combine mRNA expression, DNA methylation and microRNA (miRNA) expression data for five cancer data sets. SNF substantially outperforms single data type analysis and established integrative approaches when identifying cancer subtypes and is effective for predicting survival.

Citing Articles

iModEst: disentangling -omic impacts on gene expression variation across genes and tissues.

Sokolowski D, Mai M, Verma A, Morgenshtern G, Subasri V, Naveed H NAR Genom Bioinform. 2025; 7(1):lqaf011.

PMID: 40041206 PMC: 11879402. DOI: 10.1093/nargab/lqaf011.

MOGAN for LUAD Subtype Classification by Integrating Three Omics Data Types.

He H, Wang L, Ma M Cancer Innov. 2025; 4(2):e160.

PMID: 40026873 PMC: 11868734. DOI: 10.1002/cai2.160.

KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning.

Luo H, Yang H, Zhang G, Wang J, Luo J, Yan C Front Pharmacol. 2025; 16:1525029.

PMID: 40008124 PMC: 11850324. DOI: 10.3389/fphar.2025.1525029.

Heterogeneous Clustering of Multiomics Data for Breast Cancer Subgroup Classification and Detection.

Pateras J, Lodi M, Rana P, Ghosh P Int J Mol Sci. 2025; 26(4).

PMID: 40004168 PMC: 11855380. DOI: 10.3390/ijms26041707.

Comprehensive Evaluation of Multi-Omics Clustering Algorithms for Cancer Molecular Subtyping.

Wang J, Wang L, Liu Y, Li X, Ma J, Li M Int J Mol Sci. 2025; 26(3).

PMID: 39940732 PMC: 11816650. DOI: 10.3390/ijms26030963.

References

Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R . Missing value estimation methods for DNA microarrays. Bioinformatics. 2001; 17(6):520-5. DOI: 10.1093/bioinformatics/17.6.520. View

Barabasi A . Network medicine--from obesity to the "diseasome". N Engl J Med. 2007; 357(4):404-7. DOI: 10.1056/NEJMe078114. View

Margolin A, Bilal E, Huang E, Norman T, Ottestad L, Mecham B . Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer. Sci Transl Med. 2013; 5(181):181re1. PMC: 3897241. DOI: 10.1126/scitranslmed.3006112. View

Sturm D, Witt H, Hovestadt V, Khuong-Quang D, Jones D, Konermann C . Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell. 2012; 22(4):425-37. DOI: 10.1016/j.ccr.2012.08.024. View

Nigro J, Misra A, Zhang L, Smirnov I, Colman H, Griffin C . Integrated array-comparative genomic hybridization and expression array profiles identify clinically relevant molecular subtypes of glioblastoma. Cancer Res. 2005; 65(5):1678-86. DOI: 10.1158/0008-5472.CAN-04-2921. View

Friend S, Ideker T . Point: Are we prepared for the future doctor visit?. Nat Biotechnol. 2011; 29(3):215-8. DOI: 10.1038/nbt.1794. View

. Comprehensive genomic characterization of squamous cell lung cancers. Nature. 2012; 489(7417):519-25. PMC: 3466113. DOI: 10.1038/nature11404. View

Curtis C, Shah S, Chin S, Turashvili G, Rueda O, Dunning M . The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012; 486(7403):346-52. PMC: 3440846. DOI: 10.1038/nature10983. View

Zhang W, Ota T, Shridhar V, Chien J, Wu B, Kuang R . Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment. PLoS Comput Biol. 2013; 9(3):e1002975. PMC: 3605061. DOI: 10.1371/journal.pcbi.1002975. View

10.

Verhaak R, Hoadley K, Purdom E, Wang V, Qi Y, Wilkerson M . Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010; 17(1):98-110. PMC: 2818769. DOI: 10.1016/j.ccr.2009.12.020. View

11.

. Comprehensive molecular portraits of human breast tumours. Nature. 2012; 490(7418):61-70. PMC: 3465532. DOI: 10.1038/nature11412. View

12.

Sun S, Wong T, Zhang X, Pu J, Lee N, Day P . Protein alterations associated with temozolomide resistance in subclones of human glioblastoma cell lines. J Neurooncol. 2011; 107(1):89-100. PMC: 3273683. DOI: 10.1007/s11060-011-0729-8. View

13.

Shen R, Olshen A, Ladanyi M . Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics. 2009; 25(22):2906-12. PMC: 2800366. DOI: 10.1093/bioinformatics/btp543. View

14.

Kirk P, Griffin J, Savage R, Ghahramani Z, Wild D . Bayesian correlated clustering to integrate multiple datasets. Bioinformatics. 2012; 28(24):3290-7. PMC: 3519452. DOI: 10.1093/bioinformatics/bts595. View

15.

Wang K, Li M, Hadley D, Liu R, Glessner J, Grant S . PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res. 2007; 17(11):1665-74. PMC: 2045149. DOI: 10.1101/gr.6861907. View

16.

. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012; 487(7407):330-7. PMC: 3401966. DOI: 10.1038/nature11252. View

17.

Tusher V, Tibshirani R, Chu G . Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001; 98(9):5116-21. PMC: 33173. DOI: 10.1073/pnas.091062498. View