用廉价稀疏数据+自然图像，一键还原高精度空间转录组图谱
Sparser2Sparse: Single-shot Sparser-to-Sparse Learning for Spatial Transcriptomics Imputation with Natural Image Co-learning
- 通过自监督学习挖掘空间转录组内在结构模式
- 联合自然图像提升特征表示，实现跨域协同训练
- 适合需要低成本重建空间基因表达的生物医学研究者
空间转录组学（ST）通过在组织中实现高分辨率基因表达分析，革新了生物医学研究。然而，高分辨率ST数据成本高昂且稀缺，仍是主要挑战。我们提出单次训练的稀疏到稀疏（S2S-ST）框架，仅需一个低成本稀疏采样的ST数据集和广泛可用的自然图像即可完成联合训练。该方法包含三项创新：(1) 利用ST数据内在空间模式的稀疏到稀疏自监督学习策略；(2) 与自然图像的跨域协同学习以增强特征表示；(3) 级联数据一致性重构网络（CDCIN），迭代优化预测并保持采样基因数据的真实性。在乳腺癌、肝脏及淋巴组织等多种组织类型上的实验表明，本方法在重构精度上优于现有先进方法。该框架显著降低对高成本高分辨率数据的依赖，推动其在生物医学研究和临床应用中的广泛应用。
原文摘要 · Abstract (English)
Spatial transcriptomics (ST) has revolutionized biomedical research by enabling high resolution gene expression profiling within tissues. However, the high cost and scarcity of high resolution ST data remain significant challenges. We present Single-shot Sparser-to-Sparse (S2S-ST), a novel framework for accurate ST imputation that requires only a single and low-cost sparsely sampled ST dataset alongside widely available natural images for co-training. Our approach integrates three key innovations: (1) a sparser-to-sparse self-supervised learning strategy that leverages intrinsic spatial patterns in ST data, (2) cross-domain co-learning with natural images to enhance feature representation, and (3) a Cascaded Data Consistent Imputation Network (CDCIN) that iteratively refines predictions while preserving sampled gene data fidelity. Extensive experiments on diverse tissue types, including breast cancer, liver, and lymphoid tissue, demonstrate that our method outperforms state-of-the-art approaches in imputation accuracy. By enabling robust ST reconstruction from sparse inputs, our framework significantly reduces reliance on costly high resolution data, facilitating potential broader adoption in biomedical research and clinical applications.
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