SIGEL: a context-aware genomic representation learning framework for spatial genomics analysis
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.
Authors
- Wenlin Li (ORCID: https://orcid.org/0000-0002-0842-222X)
- Mengjin Zhu (ORCID: https://orcid.org/0000-0001-8931-5022)
- Xiaobo Sun (ORCID: https://orcid.org/0000-0001-9876-5666)
- Jing Chen (ORCID: https://orcid.org/0000-0003-0835-2396)
- Hao Wu (ORCID: https://orcid.org/0000-0003-1269-7354)
- Mengqian Huang (ORCID: https://orcid.org/0000-0002-5738-2674)
- Ziyi Wang (ORCID: https://orcid.org/0000-0003-1884-8053)
- Yucheng Xu (ORCID: https://orcid.org/0009-0004-9552-4305)
Institutions
- Zhongnan University of Economics and Law (CN)
- Shenzhen University (CN)
- Chinese Academy of Sciences (CN)
- Nankai University (CN)
- Center for Human Genetics (US)
- Chinese University of Hong Kong, Shenzhen (CN)
- Institute of Human Genetics (PL)
- Shenzhen Institutes of Advanced Technology (CN)
- Shenzhen Technology University (CN)
Publication Details
- Journal
- Genome biology
- Published
- 2025-09-22
- DOI
- https://doi.org/10.1186/s13059-025-03748-7
- Citations
- 1
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.81