用空间转录组数据提升病理图像分类，克服批次效应干扰
Towards Spatial Transcriptomics-guided Pathological Image Recognition with Batch-Agnostic Encoder
- 设计无批次依赖的对比学习框架，从多患者数据中提取一致基因信号
- 在公开数据集上显著提升病理图像亚型分类准确率，验证方法有效性
- 适合做病理图像分析与多组学融合的研究者参考
空间转录组学（ST）可同时获取病理图像与带有空间坐标的基因表达谱，因其与疾病亚型等病理特征密切相关，具有增强图像表征的潜力。然而，目前尚无研究将ST用于病理图像的补丁级亚型分类。主要挑战在于空间转录组存在显著的批次效应，导致难以从中提取稳定的病理特征。本文提出一种无批次依赖的对比学习框架，通过变分推断训练的基因编码器，从多个患者的基因表达中提取一致信号。实验在公开数据集上验证了该框架的有效性。代码已公开于 https://github.com/naivete5656/TPIRBAE。
原文摘要 · Abstract (English)
Spatial transcriptomics (ST) is a novel technique that simultaneously captures pathological images and gene expression profiling with spatial coordinates. Since ST is closely related to pathological features such as disease subtypes, it may be valuable to augment image representation with pathological information. However, there are no attempts to leverage ST for image recognition ({\it i.e,} patch-level classification of subtypes of pathological image.). One of the big challenges is significant batch effects in spatial transcriptomics that make it difficult to extract pathological features of images from ST. In this paper, we propose a batch-agnostic contrastive learning framework that can extract consistent signals from gene expression of ST in multiple patients. To extract consistent signals from ST, we utilize the batch-agnostic gene encoder that is trained in a variational inference manner. Experiments demonstrated the effectiveness of our framework on a publicly available dataset. Code is publicly available at https://github.com/naivete5656/TPIRBAE
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