基于Hi-C数据的拓扑域网络建模
Network modelling of topological domains using Hi-C data
摘要：
染色体构象捕获实验，如Hi-C，用于绘制基因组的三维空间组织。3D组织的一个特定特征被称为拓扑关联结构域（TADs），其是密集相互作用的连续染色质区域，在调节基因表达中起重要作用。已经提出了一些算法来检测TAD。特别是，Hi-C数据的结构自然激发了社区检测方法的应用。然而，社区检测的一个缺点是，大多数方法认为网络中的节点交换是理所当然的;而在这种情况下，节点，即染色体上的位置，是不可交换的。我们提出了一个网络模型，用于检测TADs使用Hi-C数据，考虑到这种非交换。此外，我们的模型明确利用细胞类型特异性CTCF结合位点作为生物学协变量，并可用于识别多种细胞类型的保守TAD。该模型导致一个可能性的目标，可以有效地通过放松优化。我们还证明了，当适当的初始化，该模型发现潜在的结构与高概率。使用模拟数据，我们展示了我们的方法的优点和流行的社区检测方法，如谱聚类，在这个应用程序中的警告。将我们的方法应用于真实的Hi-C数据，我们证明了所确定的结构域具有理想的表观遗传特征，并将其在不同的细胞类型中进行比较。
英文摘要：
Chromosome conformation capture experiments such as Hi-C are used to map the three-dimensional spatial organization of genomes. One specific feature of the 3D organization is known as topologically associating domains (TADs), which are densely interacting, contiguous chromatin regions playing important roles in regulating gene expression. A few algorithms have been proposed to detect TADs. In particular, the structure of Hi-C data naturally inspires application of community detection methods. However, one of the drawbacks of community detection is that most methods take exchangeability of the nodes in the network for granted; whereas the nodes in this case, i.e. the positions on the chromosomes, are not exchangeable. We propose a network model for detecting TADs using Hi-C data that takes into account this non-exchangeability. In addition, our model explicitly makes use of cell-type specific CTCF binding sites as biological covariates and can be used to identify conserved TADs across multiple cell types. The model leads to a likelihood objective that can be efficiently optimized via relaxation. We also prove that when suitably initialized, this model finds the underlying TAD structure with high probability. Using simulated data, we show the advantages of our method and the caveats of popular community detection methods, such as spectral clustering, in this application. Applying our method to real Hi-C data, we demonstrate the domains identified have desirable epigenetic features and compare them across different cell types.
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