Few existing methods enable the visualization of relationships between regulatory genomic activities and genome organization as captured by Hi-C experimental data. Genome-wide Hi-C datasets are often displayed using "heatmap" matrices, but it is difficult to intuit from these heatmaps which biochemical activities are compartmentalized together. High-dimensional Hi-C data vectors can alternatively be projected onto three-dimensional space using dimensionality reduction techniques. The resulting three-dimensional structures can serve as scaffolds for projecting other forms of genomic information, thereby enabling the exploration of relationships between genome organization and various genome annotations. However, while three-dimensional models are contextually appropriate for chromatin interaction data, some analyses and visualizations may be more intuitively and conveniently performed in two-dimensional space. We present a novel approach to the visualization and analysis of chromatin organization based on the Self-Organizing Map (SOM). The SOM algorithm provides a two-dimensional manifold which adapts to represent the high dimensional chromatin interaction space. The resulting data structure can then be used to assess relationships between regulatory genomic activities and chromatin interactions. For example, given a set of genomic coordinates corresponding to a given biochemical activity, the degree to which this activity is segregated or compartmentalized in chromatin interaction space can be intuitively visualized on the 2D SOM grid and quantified using Lorenz curve analysis. We demonstrate our approach for exploratory analysis of genome compartmentalization in a high-resolution Hi-C dataset from the human GM12878 cell line. Our SOM-based approach provides an intuitive visualization of the large-scale structure of Hi-C data and serves as a platform for integrative analyses of the relationships between various genomic activities and genome organization.

中文翻译：

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使用自组织图评估染色质相互作用与调节基因组活动之间的关系

现有的方法很少能够将 Hi-C 实验数据捕获的调控基因组活动和基因组组织之间的关系可视化。全基因组 Hi-C 数据集通常使用“热图”矩阵显示，但很难从这些热图中直观地看出哪些生化活动被划分在一起。也可以使用降维技术将高维 Hi-C 数据向量投影到三维空间。由此产生的三维结构可以作为投射其他形式基因组信息的支架，从而能够探索基因组组织和各种基因组注释之间的关系。然而，虽然三维模型在上下文中适用于染色质相互作用数据，一些分析和可视化可能在二维空间中更直观、更方便地进行。我们提出了一种基于自组织图 (SOM) 的染色质组织可视化和分析的新方法。SOM 算法提供了一个二维流形，适用于表示高维染色质相互作用空间。然后可以使用由此产生的数据结构来评估调节基因组活动和染色质相互作用之间的关系。例如，给定一组对应于给定生化活动的基因组坐标，该活动在染色质相互作用空间中被隔离或划分的程度可以在 2D SOM 网格上直观地可视化，并使用洛伦兹曲线分析进行量化。我们展示了我们在来自人类 GM12878 细胞系的高分辨率 Hi-C 数据集中对基因组区划进行探索性分析的方法。我们基于 SOM 的方法提供了 Hi-C 数据大规模结构的直观可视化，并作为综合分析各种基因组活动与基因组组织之间关系的平台。