ScholarWorks@숙명여자대학교

초록

Identifying cell types is crucial for characterizing biological phenomena in tissues at the single-cell level and understanding intracellular and intercellular interactions. Recent studies have introduced computational tools for cell type prediction using machine learning (ML), tailored for single-cell and spatial transcriptomics datasets. However, these approaches primarily focus on leveraging the gene expression profiles of individual cells, often overlooking the interactions between neighboring cells. Such interactions are vital, as they activate signaling pathways and coordinate gene expression. In this study, we introduce CSNpred, a cell type prediction framework that integrates graph convolutional networks with cell-specific network construction for transcriptomics data. Our model identifies neighboring cells with similar gene expression patterns, particularly those within close spatial proximity (when applicable) and constructs a network for each cell. This approach enables the learning of graph embeddings that account for both the cell's gene expression and that of its neighbors. CSNpred outperforms the state-of-the-art cell type identification method and widely used ML-based classifiers, demonstrating superior prediction performance across various scenarios. Furthermore, we examined the role of cell-specific network construction in enhancing the classifier's robustness, further validating its efficacy. CSNpred is publicly available at https://github.com/cbi-bioinfo/CSNpred.

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