  In recent years, the field of single-cell data analysis has seen a marked
advancement in the development of clustering methods. Despite advancements,
most of these algorithms still concentrate on analyzing the provided
single-cell matrix data. However, in medical applications, single-cell data
often involves a wealth of exogenous information, including gene networks.
Overlooking this aspect could lead to information loss and clustering results
devoid of significant clinical relevance. An innovative single-cell deep
clustering method, incorporating exogenous gene information, has been proposed
to overcome this limitation. This model leverages exogenous gene network
information to facilitate the clustering process, generating discriminative
representations. Specifically, we have developed an attention-enhanced graph
autoencoder, which is designed to efficiently capture the topological features
between cells. Concurrently, we conducted a random walk on an exogenous
Protein-Protein Interaction (PPI) network, thereby acquiring the gene's
topological features. Ultimately, during the clustering process, we integrated
both sets of information and reconstructed the features of both cells and genes
to generate a discriminative representation. Extensive experiments have
validated the effectiveness of our proposed method. This research offers
enhanced insights into the characteristics and distribution of cells, thereby
laying the groundwork for early diagnosis and treatment of diseases.
