Central-to-Local Adaptive Generative Diffusion Framework for Improving Gene Expression Prediction in Data-Limited Spatial Transcriptomics
Summary: arXiv:2603.26827v1 Announce Type: cross
Abstract
Spatial Transcriptomics (ST) has emerged as a revolutionary technique, providing spatially resolved gene expression profiles that maintain the integrity of tissue architecture. This capability allows for a deeper molecular analysis in a histological context, which is critical for understanding various biological processes and diseases. However, the adoption of ST is hindered by several challenges, including high costs, limited throughput, and restrictions on data sharing. These factors contribute to a significant scarcity of data, which in turn limits the development of robust computational models necessary for accurate gene expression predictions.
Introduction to C2L-ST
To confront these limitations, researchers have introduced the Central-to-Local adaptive generative diffusion framework for Spatial Transcriptomics (C2L-ST). This innovative framework integrates large-scale morphological priors with limited molecular guidance, thereby enhancing the predictive capabilities of gene expression models in data-limited scenarios.
Methodology
The C2L-ST framework operates through a two-step process:
- Global Central Model Pretraining: The framework begins with a global central model that is pretrained on extensive histopathology datasets. This step is crucial for learning transferable morphological representations that can be applied across different contexts.
- Local Model Adaptation: Following the initial pretraining, local models specific to institutions are adapted. This adaptation is achieved through lightweight gene-conditioned modulation, utilizing a minimal number of paired image-gene spots. This dual approach ensures that while the model retains robustness through its global training, it also gains specificity through localized adjustments.
Results
The results of implementing the C2L-ST framework are promising. The generated images demonstrate:
- High visual and structural fidelity.
- Accurate reproduction of cellular composition.
- Strong embedding overlap with real data across multiple organs, indicating both realism and diversity.
When these synthetic image-gene pairs are integrated into downstream training processes, they significantly enhance gene expression prediction accuracy and spatial coherence. Remarkably, the performance achieved with synthetic data is comparable to that obtained using real data, while utilizing only a fraction of the sampled spots.
Conclusion
The C2L-ST framework represents a scalable and data-efficient solution for molecular-level data augmentation. Its domain-adaptive and generalizable approach is poised to revolutionize the integration of histology and transcriptomics in spatial biology and related fields, providing researchers with powerful tools to advance our understanding of complex biological systems.