A powerful score-based statistical test for group difference in weighted biological networks

BMC Bioinformatics

Methodology Article

Fuzhong Xue¹  Zhongshang Yuan¹  Jiadong Ji¹  Xiaoshuai Zhang¹ 

[1] Department of Biostatistics, School of Public Health, Shandong University, PO Box 100, 250012, Jinan, Shandong, China;

关键词: Network medicine;    Systems epidemiology;    Score-based statistical test;    Network comparison;   

DOI  :  10.1186/s12859-016-0916-x

 received in 2015-09-11, accepted in 2016-01-29,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundComplex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. A key but inadequately addressed issue is how to test possible differences of the networks between two groups. Group-level comparison of network properties may shed light on underlying disease mechanisms and benefit the design of drug targets for complex diseases. We therefore proposed a powerful score-based statistic to detect group difference in weighted networks, which simultaneously capture the vertex changes and edge changes.ResultsSimulation studies indicated that the proposed network difference measure (NetDifM) was stable and outperformed other methods existed, under various sample sizes and network topology structure. One application to real data about GWAS of leprosy successfully identified the specific gene interaction network contributing to leprosy. For additional gene expression data of ovarian cancer, two candidate subnetworks, PI3K-AKT and Notch signaling pathways, were considered and identified respectively.ConclusionsThe proposed method, accounting for the vertex changes and edge changes simultaneously, is valid and powerful to capture the group difference of biological networks.

【 授权许可】

CC BY   
© Ji et al. 2016

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