中文标题#
超圖神經網絡從單細胞轉錄組學數據中揭示空間域
英文標題#
Hypergraph Neural Networks Reveal Spatial Domains from Single-cell Transcriptomics Data
中文摘要#
空間轉錄組數據的聚類任務至關重要。 它能夠將組織樣本分類為多種細胞亞群，從而促進對聚類的生物功能、組織重建和細胞間相互作用的分析。 許多方法利用基因表達、空間位置和組織學圖像來檢測空間域；然而，作為最先進模型的圖神經網絡（GNNs）在節點之間的成對連接假設上存在局限性。 在空間轉錄組學的域檢測中，一些細胞被發現並不直接相關，但仍被分組為同一域，這表明 GNNs 在捕捉細胞之間隱式連接方面的能力不足。 雖然圖邊僅連接兩個節點，超邊則通過其邊連接任意數量的節點，這使得超圖神經網絡（HGNNs）能夠比傳統 GNNs 捕捉和利用更豐富、更複雜的結構信息。 我們使用自編碼器來解決缺乏實際標籤的限制，這使其非常適合無監督學習。 我們的模型表現出卓越的性能，與其他方法相比，取得了最高的 iLISI 分數 1.843。 這個分數表明我們的方法識別出的細胞類型多樣性最大。 此外，我們的模型在下游聚類中優於其他方法，達到了最高的 ARI 值 0.51 和 Leiden 分數 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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