比较图元向量和点的聚类系数对差异网络的研究

Comparing graphlet orbit and clustering coefficient in differentially network

差异分析对于揭示生命体的生长、发育和衰老过程及疾病发生具有重大的意义,基于网络的差异分析方法已经成为系统生物学的一个研究热点。网络节点往往通过与局部结构作用实现某种功能,其与局部结构的关系变化,很可能影响其功能。本文利用仿真实验的方法比较了图元向量和点的聚类系数两种局部结构测度的性能,并且利用他们分别设计算法挖掘差异网络中模块化变化的基因簇。应用AGEMAP数据库中小鼠12个组织基因表达数据进行实验,大部分聚类簇都高度显著富集与衰老相关的GO项。

Differential analysis is very important for understanding the process of biological evolution and the progress of diseases. Recently, graph based differential analysis has turned to be a hot area in system biology. One node often works with its neighboring nodes, so that their functions will change if their connections change. In this paper, we compared graphlet orbit and clustering coefficient by random networks. We used them to mine within module differential co-expresssion clusters. Application to data for mice showed that most of the clusters are significant enriched in some GO terms related to aging.