Mall Raghvendra, Cerulo Luigi, Bensmail Halima, Iavarone Antonio, Ceccarelli Michele
QCRI - Qatar Computing Research Institute, HBKU, Doha, Qatar.
Department of Science and Technology, University of Sannio, Benevento, Italy.
BMC Syst Biol. 2017 Mar 4;11(1):32. doi: 10.1186/s12918-017-0412-6.
Biological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localized re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to the discovery of novel relevant signatures. Several procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks.
In this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate p-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. Moreover, the structure of the proposed algorithm allows for a faster parallelized implementation.
In the case of dense random geometric networks the proposed approach is 10-15x faster and achieves 5-10% higher AUC, Precision/Recall, and Kappa value than the state-of-the-art. We also report the application of the method to dissect the difference between the regulatory networks of IDH-mutant versus IDH-wild-type glioma cancer. In such a case our method is able to identify some recently reported master regulators as well as novel important candidates.
We show that our network differencing procedure can effectively and efficiently detect statistical significant network re-wirings in different conditions. When applied to detect the main differences between the networks of IDH-mutant and IDH-wild-type glioma tumors, it correctly selects sub-networks centered on important key regulators of these two different subtypes. In addition, its application highlights several novel candidates that cannot be detected by standard single network-based approaches.
生物网络有助于有效揭示分子相互作用的复杂结构，并发现驱动基因，尤其是在癌症背景下。例如，当癌症进展时，由于基因突变，基因表达网络可能会发生一定程度的局部重新布线。检测疾病进展引起的相互作用模式中统计上相关变化的能力，可能会导致发现新的相关特征。最近已经提出了几种程序来检测成对标记加权网络中的子网差异。
在本文中，我们基于广义汉明距离提出了一种优于现有技术的方法，用于评估两个网络之间的拓扑差异并估计其统计显著性。所提出的程序利用更有效的模型选择标准来生成统计显著性的p值，并且在计算时间和预测准确性方面比文献方法更有效。此外，所提出算法的结构允许更快的并行实现。
在密集随机几何网络的情况下，所提出的方法比现有技术快10 - 15倍，并且在AUC、精确率/召回率和卡帕值方面高出5 - 10%。我们还报告了该方法在剖析异柠檬酸脱氢酶（IDH）突变型与IDH野生型胶质瘤癌症调控网络差异方面的应用。在这种情况下，我们的方法能够识别一些最近报道的主调控因子以及新的重要候选因子。
我们表明，我们的网络差异程序能够在不同条件下有效且高效地检测出统计上显著的网络重新布线。当应用于检测IDH突变型和IDH野生型胶质瘤肿瘤网络之间的主要差异时，它正确地选择了以这两种不同亚型的重要关键调控因子为中心的子网。此外，其应用突出了一些标准单网络方法无法检测到的新候选因子。
