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Abstract：
Network properties account for the complex relationship between genes, making it easier to identify complex patterns in their interactions. In this work, we leveraged these network properties for dual purposes. First, we clustered pediatric sarcoma tumors using network information flow as a similarity metric, computed by the Wasserstein distance. We demonstrate that this approach yields the best concordance with histological subtypes, validated against three state-of-the-art methods. Second, to identify molecular targets that would be missed by more conventional methods of analysis, we applied a novel unsupervised method to cluster gene interactomes represented as networks in pediatric sarcoma. RNA-Seq data were mapped to protein-level interactomes to construct weighted networks that were then subjected to a non-Euclidean, multi-scale geometric approach centered on a discrete notion of curvature. This provides a measure of the functional association among genes in the context of their connectivity. In confirmation of the validity of this method, hierarchical clustering revealed the characteristic EWSR1-FLI1 fusion in Ewing sarcoma. Furthermore, assessing the effects of in silico edge perturbations and simulated gene knockouts as quantified by changes in curvature, we found non-trivial gene associations not previously identified.
摘要：
网络属性解释了基因之间的复杂关系，更容易识别它们相互作用中的复杂模式。在这项工作中,我们将这些网络属性用于双重目的。首先,我们使用网络信息流作为相似性度量对小儿肉瘤肿瘤进行聚类，由Wasserstein距离计算。我们证明了这种方法与组织学亚型产生最佳的一致性，对三种最先进的方法进行了验证。第二,为了确定更传统的分析方法会遗漏的分子靶标，我们应用了一种新的无监督方法对儿童肉瘤中表示为网络的基因间细胞进行聚类。将RNA-Seq数据映射到蛋白质水平的相互作用组以构建加权网络，然后对其进行非欧几里得，以曲率的离散概念为中心的多尺度几何方法。这提供了在基因连接的背景下基因之间的功能关联的量度。为了确认该方法的有效性，分层聚类揭示了尤文肉瘤中特征性的EWSR1-FLI1融合。此外，评估通过曲率变化量化的计算机边缘扰动和模拟基因敲除的影响，我们发现了以前未发现的重要基因关联.
