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XMWAS: a Data-driven Integration and Differential Network Analysis Tool

Overview
Journal Bioinformatics
Publisher Oxford University Press
Specialty Biology
Date 2017 Oct 26
PMID 29069296
Citations 98
Authors
Karan Uppal
Chunyu Ma
Young-Mi Go
Dean P Jones
Jonathan Wren
Affiliations
Soon will be listed here.
Abstract

Summary: Integrative omics is a central component of most systems biology studies. Computational methods are required for extracting meaningful relationships across different omics layers. Various tools have been developed to facilitate integration of paired heterogenous omics data; however most existing tools allow integration of only two omics datasets. Furthermore, existing data integration tools do not incorporate additional steps of identifying sub-networks or communities of highly connected entities and evaluating the topology of the integrative network under different conditions. Here we present xMWAS, a software for data integration, network visualization, clustering, and differential network analysis of data from biochemical and phenotypic assays, and two or more omics platforms.

Availability And Implementation: https://kuppal.shinyapps.io/xmwas (Online) and https://github.com/kuppal2/xMWAS/ (R).

Contact: kuppal2@emory.edu.

Supplementary Information: Supplementary data are available at Bioinformatics online.

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References
1.
Le Cao K, Gonzalez I, Dejean S . integrOmics: an R package to unravel relationships between two omics datasets. Bioinformatics. 2009; 25(21):2855-6. PMC: 2781751. DOI: 10.1093/bioinformatics/btp515. View
2.
Odibat O, Reddy C . Ranking differential hubs in gene co-expression networks. J Bioinform Comput Biol. 2012; 10(1):1240002. DOI: 10.1142/S0219720012400021. View
3.
Hawkins R, Hon G, Ren B . Next-generation genomics: an integrative approach. Nat Rev Genet. 2010; 11(7):476-86. PMC: 3321268. DOI: 10.1038/nrg2795. View
4.
Yang Z, Algesheimer R, Tessone C . A Comparative Analysis of Community Detection Algorithms on Artificial Networks. Sci Rep. 2016; 6:30750. PMC: 4967864. DOI: 10.1038/srep30750. View
5.
Li S, Park Y, Duraisingham S, Strobel F, Khan N, Soltow Q . Predicting network activity from high throughput metabolomics. PLoS Comput Biol. 2013; 9(7):e1003123. PMC: 3701697. DOI: 10.1371/journal.pcbi.1003123. View