这项研究介绍了一个名为 DOLPHIN 的深度学习框架，该框架旨在通过整合 外显子水平和剪接点读取数据 来提升单细胞RNA测序（scRNA-seq）分析的精度。传统的基因计数方法往往忽略这些详细信息，从而限制了对细胞异质性和替代剪接模式的理解。DOLPHIN 将基因表示为图形结构，并利用 变分图自编码器 生成更精确的细胞嵌入，从而在 细胞聚类、生物标志物发现和替代剪接检测 等方面表现出卓越的性能。该方法能够识别基因水平分析中常被掩盖的 细微转录组差异，并已在多种scRNA-seq数据集上进行了验证，展示了其在揭示疾病机制和治疗靶点方面的新潜力。References: Song K, Zheng Y, Zhao B, et al. DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads[J]. Nature Communications, 2025, 16(1): 6202.
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