用病理图像预测基因表达，提升癌症研究效率
Spatial Transcriptomics Expression Prediction from Histopathology Based on Cross-Modal Mask Reconstruction and Contrastive Learning
- 结合跨模态掩码重建与对比学习，从病理图预测空间基因表达
- 在六类疾病数据上，高表达/可变/标志基因预测准确率分别提升6.27%~11.26%
- 适用于小样本数据，能辅助癌症组织定位，适合生物医学研究者
空间转录组学可捕捉不同空间位置的基因表达水平，广泛应用于肿瘤微环境分析和病理分子分型，为癌症基因表达与临床诊断提供关键信息。由于数据获取成本高，大规模空间转录组数据仍难获得。本研究提出一种基于对比学习的深度学习方法，从全切片图像预测空间分辨基因表达。在六个不同疾病数据集上的评估表明，相比现有方法，本方法在高表达基因、高变异性基因及标志基因的预测中，皮尔逊相关系数（PCC）分别提升6.27%、6.11%和11.26%。进一步分析显示，该方法能有效保留基因间相关性，适用于样本量有限的数据集，并展现出基于生物标志物表达进行癌症组织定位的潜力。
原文摘要 · Abstract (English)
Spatial transcriptomics is a technology that captures gene expression levels at different spatial locations, widely used in tumor microenvironment analysis and molecular profiling of histopathology, providing valuable insights into resolving gene expression and clinical diagnosis of cancer. Due to the high cost of data acquisition, large-scale spatial transcriptomics data remain challenging to obtain. In this study, we develop a contrastive learning-based deep learning method to predict spatially resolved gene expression from whole-slide images. Evaluation across six different disease datasets demonstrates that, compared to existing studies, our method improves Pearson Correlation Coefficient (PCC) in the prediction of highly expressed genes, highly variable genes, and marker genes by 6.27%, 6.11%, and 11.26% respectively. Further analysis indicates that our method preserves gene-gene correlations and applies to datasets with limited samples. Additionally, our method exhibits potential in cancer tissue localization based on biomarker expression.
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