中文标题#
超图神经网络从单细胞转录组学数据中揭示空间域
英文标题#
Hypergraph Neural Networks Reveal Spatial Domains from Single-cell Transcriptomics Data
中文摘要#
空间转录组数据的聚类任务至关重要。 它能够将组织样本分类为多种细胞亚群，从而促进对聚类的生物功能、组织重建和细胞间相互作用的分析。 许多方法利用基因表达、空间位置和组织学图像来检测空间域；然而，作为最先进模型的图神经网络（GNNs）在节点之间的成对连接假设上存在局限性。 在空间转录组学的域检测中，一些细胞被发现并不直接相关，但仍被分组为同一域，这表明 GNNs 在捕捉细胞之间隐式连接方面的能力不足。 虽然图边仅连接两个节点，超边则通过其边连接任意数量的节点，这使得超图神经网络（HGNNs）能够比传统 GNNs 捕捉和利用更丰富、更复杂的结构信息。 我们使用自编码器来解决缺乏实际标签的限制，这使其非常适合无监督学习。 我们的模型表现出卓越的性能，与其他方法相比，取得了最高的 iLISI 分数 1.843。 这个分数表明我们的方法识别出的细胞类型多样性最大。 此外，我们的模型在下游聚类中优于其他方法，达到了最高的 ARI 值 0.51 和 Leiden 分数 0.60。
英文摘要#
The task of spatial clustering of transcriptomics data is of paramount importance. It enables the classification of tissue samples into diverse subpopulations of cells, which, in turn, facilitates the analysis of the biological functions of clusters, tissue reconstruction, and cell-cell interactions. Many approaches leverage gene expressions, spatial locations, and histological images to detect spatial domains; however, Graph Neural Networks (GNNs) as state of the art models suffer from a limitation in the assumption of pairwise connections between nodes. In the case of domain detection in spatial transcriptomics, some cells are found to be not directly related. Still, they are grouped as the same domain, which shows the incapability of GNNs for capturing implicit connections among the cells. While graph edges connect only two nodes, hyperedges connect an arbitrary number of nodes along their edges, which lets Hypergraph Neural Networks (HGNNs) capture and utilize richer and more complex structural information than traditional GNNs. We use autoencoders to address the limitation of not having the actual labels, which are well-suited for unsupervised learning. Our model has demonstrated exceptional performance, achieving the highest iLISI score of 1.843 compared to other methods. This score indicates the greatest diversity of cell types identified by our method. Furthermore, our model outperforms other methods in downstream clustering, achieving the highest ARI values of 0.51 and Leiden score of 0.60.
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