标题： 基于随机游走的scRNA-seq数据超图表示用于改进聚类 显示英文标题

标题： Hypergraph Representations of scRNA-seq Data for Improved Clustering with Random Walks

摘要： 对单细胞RNA测序数据的分析通常通过网络投影（如共表达网络）进行，主要是由于下游任务中网络分析工具的丰富可用性。 然而，这种方法有几个局限性：高阶信息的丢失，由于将稀疏数据转换为全连接网络而导致的数据表示效率低下，以及由于零值膨胀导致的共表达估计过高。 为了解决这些限制，我们提出将scRNA-seq表达数据概念化为超图，超图是一种广义图，其中超边可以连接多于两个顶点。 在scRNA-seq数据的背景下，超图的节点代表细胞，边代表基因。 每个超边连接其对应基因活跃表达的所有细胞，并记录该基因在不同细胞中的表达情况。 这种超图概念化使我们能够在不丢失信息的情况下探索共表达网络中的成对相互作用之外的多向关系。 我们提出了两种新的聚类方法：(1) 双重要性偏好超图随机游走（DIPHW）和(2) 共表达与记忆集成的双重要性偏好超图随机游走（CoMem-DIPHW）。 它们在模拟和真实scRNA-seq数据集上均优于现有方法。 当数据模块性较弱时，我们提出的方法带来的改进尤其显著。 此外，CoMem-DIPHW结合了从单细胞丰度计数数据中得到的基因共表达网络、细胞共表达网络以及细胞-基因表达超图，用于嵌入计算。 这种方法同时考虑了来自单细胞水平基因表达的局部信息以及来自两个共表达网络中成对相似性的全局信息。 显示英文摘要

摘要： Analysis of single-cell RNA sequencing data is often conducted through network projections such as coexpression networks, primarily due to the abundant availability of network analysis tools for downstream tasks. However, this approach has several limitations: loss of higher-order information, inefficient data representation caused by converting a sparse dataset to a fully connected network, and overestimation of coexpression due to zero-inflation. To address these limitations, we propose conceptualizing scRNA-seq expression data as hypergraphs, which are generalized graphs in which the hyperedges can connect more than two vertices. In the context of scRNA-seq data, the hypergraph nodes represent cells and the edges represent genes. Each hyperedge connects all cells where its corresponding gene is actively expressed and records the expression of the gene across different cells. This hypergraph conceptualization enables us to explore multi-way relationships beyond the pairwise interactions in coexpression networks without loss of information. We propose two novel clustering methods: (1) the Dual-Importance Preference Hypergraph Walk (DIPHW) and (2) the Coexpression and Memory-Integrated Dual-Importance Preference Hypergraph Walk (CoMem-DIPHW). They outperform established methods on both simulated and real scRNA-seq datasets. The improvement brought by our proposed methods is especially significant when data modularity is weak. Furthermore, CoMem-DIPHW incorporates the gene coexpression network, cell coexpression network, and the cell-gene expression hypergraph from the single-cell abundance counts data altogether for embedding computation. This approach accounts for both the local level information from single-cell level gene expression and the global level information from the pairwise similarity in the two coexpression networks.