Differentially mutated subnetworks discovery
Differentially mutated subnetworks discovery
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DOI：
10.1186/s13015-019-0146-7
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发表时间：
2019-03-30
影响因子：
1
通讯作者：
Vandin, Fabio
中科院分区：
文献类型：
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作者：
Hajkarim, Morteza Chalabi;Upfal, Eli;Vandin, Fabio
问题我们研究的问题是识别一个大型基因-基因相互作用网络的差异突变子网络，即在两组癌症样本中显示突变频率显著差异的子网络。我们正式定义了相关的计算问题，并证明了该问题是np困难的。我们提出了一种新颖而高效的算法，称为DAMOKLE，用于识别两组癌症样本的全基因组突变数据的差异突变子网络。我们证明，当数据来自合理的生成模型时，DAMOKLE识别出突变频率具有统计显著差异的子网，只要有足够的样本可用。实验结果我们在模拟和真实数据上测试DAMOKLE，表明DAMOKLE确实发现了突变频率存在显著差异的子网络，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。
ProblemWe 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.AlgorithmWe 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 resultsWe 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.