用病理图像预测基因表达趋势，抗噪声更准
Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics
- 不学绝对值，改学基因间相对表达规律
- 在真实与合成数据上显著降低误差，提升稳定性
- 适合做空间转录组分析的科研人员参考
从病理图像估计基因表达有望降低RNA测序成本。现有方法多使用点对点损失函数来最小化预测值与绝对表达值之间的差异。然而，由于测序技术复杂及细胞间固有变异，观测到的基因表达存在随机噪声和批次效应，准确估计绝对表达值仍具挑战。为此，我们提出学习相对表达模式的新目标：尽管绝对值受批次效应和噪声影响，基因间的相对表达模式在不同实验中保持一致。基于此假设，我们构建了名为STRank的新损失函数，对噪声和批次效应具有鲁棒性。在合成数据集和真实数据集上的实验验证了该方法的有效性。代码已开源：https://github.com/naivete5656/STRank。
原文摘要 · Abstract (English)
Gene expression estimation from pathology images has the potential to reduce the RNA sequencing cost. Point-wise loss functions have been widely used to minimize the discrepancy between predicted and absolute gene expression values. However, due to the complexity of the sequencing techniques and intrinsic variability across cells, the observed gene expression contains stochastic noise and batch effects, and estimating the absolute expression values accurately remains a significant challenge. To mitigate this, we propose a novel objective of learning relative expression patterns rather than absolute levels. We assume that the relative expression levels of genes exhibit consistent patterns across independent experiments, even when absolute expression values are affected by batch effects and stochastic noise in tissue samples. Based on the assumption, we model the relation and propose a novel loss function called STRank that is robust to noise and batch effects. Experiments using synthetic datasets and real datasets demonstrate the effectiveness of the proposed method. The code is available at https://github.com/naivete5656/STRank.
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