对比自适应多模态掩码自编码器用于空间转录组学
Contrastive and Adaptive Multi-modal Masked Autoencoder for Spatial Transcriptomics
AI总结：
提出一种基于掩码自编码器的空间插补方法，利用对比学习和自适应锚点选择，从少量基因表达和H&E图像预测全切片基因表达，在10%转录组覆盖下仍优于现有方法。
摘要：
空间转录组学（ST）的高成本促使大量研究直接从H&E组织学图像预测基因表达。然而，该预测任务存在固有限制，因为仅凭组织形态学不足以完全解析潜在的基因表达。为解决此限制，最近一项研究利用部分基因表达与组织学图像共同指导预测过程。基于这一范式，我们将预测任务视为空间插补问题，采用掩码自编码器（MAE）利用一小部分基因表达作为遗传锚点来推断全切片基因表达谱。具体而言，我们提出生物显著性评分和学习排序策略，自适应地识别组织中最具信息量的点。基于这些识别点，我们的框架选择连续区域作为遗传锚点，以确保适用于实际ST分析硬件。为有效利用这些锚点，我们设计了一个跨模态联合编码器，整合视觉和遗传模态。通过对比学习将所选锚点与其对应视觉特征对齐，该编码器生成鲁棒的联合表示，以准确预测全切片的基因表达。值得注意的是，我们的框架在仅组织学预测和空间插补任务中均持续超越现有方法，即使没有遗传锚点也能实现更高精度，且在仅10%转录组覆盖下表现更优。代码见：此 https URL。
英文摘要：
The high cost of spatial transcriptomics (ST) has driven extensive studies into predicting gene expression directly from H&E histology images. However, this prediction task faces an inherent limitation, as tissue morphology alone provides insufficient information to fully resolve underlying gene expression. To address this limitation, a recent study leverages partial gene expression to guide the prediction process alongside histology images. Building on this paradigm, we approach the prediction task as a spatial imputation problem, employing a Masked Autoencoder (MAE) to utilize a small fraction of gene expression as genetic anchors for inferring whole-slide gene expression profiles. Specifically, we propose a bio-saliency score and a learning-to-rank strategy to adaptively identify the most informative spots within the tissue. Based on these identified spots, our framework selects contiguous regions as genetic anchors to ensure suitability for real-world ST profiling hardware. To effectively leverage these anchors, we design a cross-modal joint encoder that integrates visual and genetic modalities. By aligning the selected anchors with their corresponding visual features via contrastive learning, the encoder generates robust joint representations to accurately predict gene expression across the whole slide. Notably, our framework consistently surpasses existing methods in both histology-only prediction and spatial imputation, achieving superior accuracy even without genetic anchors and further excelling with as little as 10% transcriptomic coverage. Our code is available at https://github.com/Kyyle2114/CAMMST.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
