SIGEL: a context-aware genomic representation learning framework for spatial genomics analysis

作者：Wenlin Li, Maocheng Zhu, Yucheng Xu, Mengqian Huang, Ziyi Wang, Jing Chen, Hao Wu, Xiaobo Sun · 发表于：Genome biology · 年份：2025 · DOI：10.1186/s13059-025-03748-7 · 被引用次数：4 · 研究领域：Single-cell and spatial transcriptomics、Gene expression and cancer classification、Immune responses and vaccinations

Spatial transcriptomics (ST) integrates spatial information into genomics, yet methods for generating spatially-informed gene representations are limited and computationally intensive. We present SIGEL, a cost-effective framework that derives gene manifolds from ST data by exploiting spatial genomic context. The resulting SIGEL-generated gene representations (SGRs) are context-aware, biologically meaningful, and robust across samples, making them highly effective for key downstream tasks, including imputing missing genes, detecting spatial expression patterns, identifying disease-related genes and interactions, and improving spatial clustering. Extensive experiments across diverse ST datasets validate SIGEL's effectiveness and highlight its potential in advancing spatial genomics research.