PROBLEM We study the problem of identifying differentially mutated subnetworks of a large gene-gene interaction network, that is, subnetworks that display a significant difference in mutation frequency in two sets of cancer samples. We formally define the associated computational problem and show that the problem is NP-hard. ALGORITHM We propose a novel and efficient algorithm, called DAMOKLE, to identify differentially mutated subnetworks given genome-wide mutation data for two sets of cancer samples. We prove that DAMOKLE identifies subnetworks with statistically significant difference in mutation frequency when the data comes from a reasonable generative model, provided enough samples are available. EXPERIMENTAL RESULTS We test DAMOKLE on simulated and real data, showing that DAMOKLE does indeed find subnetworks with significant differences in mutation frequency and that it provides novel insights into the molecular mechanisms of the disease not revealed by standard methods.

中文翻译：

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差异突变子网络发现。

问题我们研究识别大型基因-基因相互作用网络的差异突变子网络的问题，即在两组癌症样本中显示突变频率显着差异的子网络。我们正式定义了相关的计算问题，并表明该问题是 NP 困难的。算法 我们提出了一种新颖且有效的算法，称为 DAMOKLE，根据两组癌症样本的全基因组突变数据来识别差异突变子网络。我们证明，当数据来自合理的生成模型时，如果有足够的样本可用，DAMOKLE 可以识别突变频率具有统计显着差异的子网络。实验结果我们在模拟和真实数据上测试了 DAMOKLE，表明 DAMOKLE 确实发现了突变频率存在显着差异的子网络，并且它为标准方法未揭示的疾病分子机制提供了新的见解。