将细胞内和细胞间基因-基因相互作用整合到深度组学数据分析中以增强单细胞癌症生物学研究

Single-cell RNA sequencing (scRNA-seq) is widely used in cancer research to dissect tumor heterogeneity, characterize malignant and immune cell states, and identify dysregulated regulatory programs. However, accurately accounting the gene-gene interactions in cellular samples remains challenging due to the high dimensionality and nonstructured sequences of single-cell data. Existing Transformer-based approaches often rely on oversimplified gene-embedding strategies-such as ordering, coarse value binning, or direct value projection-that reduce biological resolution and overlook key regulatory dependencies. Moreover, most current methods emphasize intra-cell interactions while neglecting population-level patterns that are essential for understanding tumor microenvironmental regulation. To address these limitations, we propose a novel dual-branch Transformer framework that explicitly integrates intra-cell and inter-cell gene-gene interactions. The method comprises two complementary branches: (1) an intra-cell interaction branch that uses a Transformer augmented with interaction-aware embeddings to capture fine-grained regulatory relationships within individual cells based on graph-derived gene representations; and (2) an inter-cell interaction branch that applies a Vision Transformer (ViT) to image-based representations of single-cell profiles, spatially organized to reflect global gene-gene interaction structures across cell populations. A cross-attention module links the two branches, enabling coordinated learning between intracellular and intercellular regulatory signals. Extensive evaluation across diverse downstream tasks-such as cell-type classification, including cancer cell recognition and characterization; gene regulatory network inference; and protein abundance prediction-demonstrates that this interaction-aware architecture consistently outperforms state-of-the-art approaches. Notably, the framework achieves approximately a 30% average improvement in protein abundance prediction, a 4% improvement in cell-type classification accuracy, and a 4% improvement in gene regulatory network inference performance. By jointly capturing cell-intrinsic regulatory signals and population-level interaction patterns, the proposed framework offers a powerful computational strategy for dissecting tumor heterogeneity and characterizing regulatory interactions between malignant and microenvironmental cell populations.