High-order Topology for Deep Single-Cell Multiview Fuzzy Clustering

Single-cell multiview clustering is essential for analyzing the different cell subtypes of the same cell from different views. Some attempts have been made, but most of these models still struggle to handle single-cell sequencing data, primarily due to their nonspecific design for cellular data. We observe that such data distinctively exhibits: 1) a profusion of high-order topological correlations, 2) a disparate distribution of information across different views, and 3) inherent fuzzy characteristics, indicating a cell's potential to associate with multiple cluster identities. Neglecting these key cellular patterns could significantly impair medical clustering. In response, we propose a specialized application of fuzzy clustering for single-cell sequencing data, namely, the deep single-cell multiview fuzzy clustering method. Concretely, we employ a random walk technique to capture high-order topological relationships on the cell graph and have developed a cross-view information aggregation mechanism that adaptively assigns weights to different views. Furthermore, to accurately reflect the dynamic insight in cellular development, we propose a deep fuzzy clustering strategy that allows cells to associate with diverse clusters. Extensive experiments conducted on three real-world single-cell multiview datasets demonstrate our method's superior performance.