title: A powerful score-based statistical test for group difference in weighted biological networks.
abstract: BACKGROUND
  Complex 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.
  
  
  RESULTS
  Simulation 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.
  
  
  CONCLUSIONS
  The proposed method, accounting for the vertex changes and edge changes simultaneously, is valid and powerful to capture the group difference of biological networks.
authors: Department of Biostatistics, School of Public Health, Shandong University, PO Box 100, Jinan, 250012, Shandong, China. jjdjijiadong@163.com.
authors: Jiadong
authors: J
authors: Ji
authors: Department of Biostatistics, School of Public Health, Shandong University, PO Box 100, Jinan, 250012, Shandong, China. yuanzhongshang@sdu.edu.cn.
authors: Zhongshang
authors: Z
authors: Yuan
authors: Department of Biostatistics, School of Public Health, Shandong University, PO Box 100, Jinan, 250012, Shandong, China. zhxiaoshuai@hotmail.com.
authors: Xiaoshuai
authors: X
authors: Zhang
authors: Department of Biostatistics, School of Public Health, Shandong University, PO Box 100, Jinan, 250012, Shandong, China. xuefzh@sdu.edu.cn.
authors: Fuzhong
authors: F
authors: Xue
mesh_terms: D004843:Epistasis, Genetic; D005260:Female; D053263:Gene Regulatory Networks; D006801:Humans; D007918:Leprosy / Q000235:genetics*; D015233:Models, Statistical; D010051:Ovarian Neoplasms / Q000235:genetics*; D015398:Signal Transduction