GraPhyC: Using Consensus to Infer Tumor Evolution
GraPhyC: Using Consensus to Infer Tumor Evolution
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DOI：
10.1109/tcbb.2020.3029689
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Kiya W. Govek;Camden Sikes;Yangqiaoyu Zhou;Layla Oesper
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作者：
Kiya W. Govek;Camden Sikes;Yangqiaoyu Zhou;Layla Oesper
我们考虑从一组冲突的输入树中找到一致的肿瘤进化树的问题。与传统的系统发生树相比，我们考虑的肿瘤树没有应用于每个树的叶子的相同的标签集。我们描述了这些肿瘤树之间的几个距离措施。我们的GraPhyC算法使用加权有向图解决了共识问题，其中顶点是突变集，边缘基于在输入树中观察到其组成突变之间的亲本关系的次数进行加权。我们找到了一个最小的重量生成树形图，并证明了它最小化的总距离，我们的距离措施之一，所有输入树。我们还描述了我们的GraPhyC方法的几个扩展。在模拟数据上，我们表明，GraPhyC优于基线方法，并证明GraPhyC可以是一种有效的手段，计算质心的k-中位数聚类。我们分析了两个真实的测序数据集，发现GraPhyC能够识别不包括在输入树集合中的树，但包含由该肿瘤的其他报告的进化重建支持的特征。
We consider the problem of finding a consensus tumor evolution tree from a set of conflicting input trees. In contrast to traditional phylogenetic trees, the tumor trees we consider do not have the same set of labels applied to the leaves of each tree. We describe several distance measures between these tumor trees. Our GraPhyC algorithm solves the consensus problem using a weighted directed graph where vertices are sets of mutations and edges are weighted based on the number of times a parental relationship is observed between their constituent mutations in the input trees. We find a minimum weight spanning arborescence in this graph and prove that it minimizes the total distance to all input trees for one of our distance measures. We also describe several extensions of our GraPhyC approach. On simulated data we show that GraPhyC outperforms a baseline method and demonstrate that GraPhyC can be an effective means of computing centroids in k-medians clustering. We analyze two real sequencing datasets and find that GraPhyC is able to identify a tree not included in the set of input trees, but that contains characteristics supported by other reported evolutionary reconstructions of this tumor.