Sun Shuli, Liu Jixin, Li Guojun, Liu Bingqiang
School of Mathematics, Shandong University, Jinan, 250100, China.
Research Center for Mathematics and Interdisciplinary Sciences (Frontiers Science Center for Nonlinear Expectations), Shandong University, Qingdao, 266237, China.
Genome Biol. 2025 Jun 3;26(1):153. doi: 10.1186/s13059-025-03631-5.
The rapid advancements in spatially resolved transcriptomics (SRT) enable the characterization of gene expressions while preserving spatial information. However, high dropout rates and noise hinder accurate spatial domain identification for understanding tissue architecture. We present DeepGFT, a method that simultaneously models spot-wise and gene-wise relationships by integrating deep learning with graph Fourier transform for spatial domain identification. Benchmarking results demonstrate the superiority of DeepGFT over existing methods. DeepGFT detects tumor substructures with immune-related differences in human breast cancer, identifies the complex germinal centers accurately in human lymph node, and accurately reveals the developmental changes in 3D Drosophila data.
空间分辨转录组学（SRT）的快速发展使得在保留空间信息的同时能够对基因表达进行表征。然而，高缺失率和噪声阻碍了为理解组织结构而进行的准确空间域识别。我们提出了DeepGFT，这是一种通过将深度学习与图傅里叶变换相结合来进行空间域识别的方法，它同时对逐点和逐基因关系进行建模。基准测试结果证明了DeepGFT相对于现有方法的优越性。DeepGFT在人类乳腺癌中检测出具有免疫相关差异的肿瘤亚结构，在人类淋巴结中准确识别出复杂的生发中心，并准确揭示了三维果蝇数据中的发育变化。
