BACKGROUND To identify and prioritize the influential hub genes in a gene-set or biological pathway, most analyses rely on calculation of marginal effects or tests of statistical significance. These procedures may be inappropriate since hub nodes are common connection points and therefore may interact with other nodes more often than non-hub nodes do. Such dependence among gene nodes can be conjectured based on the topology of the pathway network or the correlation between them. RESULTS Here we develop a pathway activity score incorporating the marginal (local) effects of gene nodes as well as intra-network affinity measures. This score summarizes the expression levels in a gene-set/pathway for each sample, with weights on local and network information, respectively. The score is next used to examine the impact of each node through a leave-one-out evaluation. To illustrate the procedure, two cancer studies, one involving RNA-Seq from breast cancer patients with high-grade ductal carcinoma in situ and one microarray expression data from ovarian cancer patients, are used to assess the performance of the procedure, and to compare with existing methods, both ones that do and do not take into consideration correlation and network information. The hub nodes identified by the proposed procedure in the two cancer studies are known influential genes; some have been included in standard treatments and some are currently considered in clinical trials for target therapy. The results from simulation studies show that when marginal effects are mild or weak, the proposed procedure can still identify causal nodes, whereas methods relying only on marginal effect size cannot. CONCLUSIONS The NetworkHub procedure proposed in this research can effectively utilize the network information in combination with local effects derived from marker values, and provide a useful and complementary list of recommendations for prioritizing causal hubs.

中文翻译：

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网络中枢与网络内节点之间的基因调控优先级。

背景技术为了鉴定基因集或生物途径中的有影响力的中枢基因并确定其优先级，大多数分析依靠边际效应的计算或统计显着性检验。这些过程可能是不合适的，因为集线器节点是公共连接点，因此与非集线器节点进行交互的频率可能更高。可以基于通路网络的拓扑或它们之间的相关性推测基因节点之间的这种依赖性。结果在这里，我们开发了一种途径活动评分，该评分包含了基因节点的边缘（局部）效应以及网络内亲和力度量。该分数总结了每个样品在基因组/途径中的表达水平，分别带有本地信息和网络信息的权重。分数随后用于通过留一法评估来检查每个节点的影响。为了说明该过程，两项癌症研究（其中一项涉及乳腺癌和原发性高级导管癌患者的RNA-Seq研究，一项涉及卵巢癌患者的微阵列表达数据）用于评估该过程的效果，并与现有方法，无论是否考虑相关性和网络信息。在两项癌症研究中，通过拟议的程序确定的枢纽节点是已知的有影响力的基因。一些已被纳入标准治疗，而一些目前已在针对靶标治疗的临床试验中考虑。模拟研究的结果表明，当边际影响为轻度或微弱时，建议的程序仍然可以识别因果关系，而仅依靠边际效应大小的方法则不能。结论本研究中提出的NetworkHub程序可以有效地利用网络信息，并结合从标记值得出的局部效应，并提供有用且互补的建议列表，以优先考虑因果中心。