基于bulk RNA-seq引导的多模态空间转录组学检测人类癌症异常区域
Bulk RNA-seq Guided Multi-modal Detection of Anomalous Regions in Human Cancer via Spatial Transcriptomics
将病理图像与空间转录组数据通过最优传输对齐基因模块，为多模态医学图像分析提供了一种跨模态对齐与融合的通用思路，可借鉴到其他需要整合组学与影像的任务中。
原文依据 we design an Optimal Transportation-based Gene Module Alignment (OTGMA) approach to align ST data with patient-level bulk RNA-seq data
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论空间转录组学（ST）已成为组织分析领域的一种革命性方法，能够提供空间分辨的分子见解，用于识别人类癌症中的异常区域（AR）。当前基于ST的AR检测方法仅关注局部组织斑点的分子特征，忽略了匹配的bulk RNA-seq数据所包含的关键诊断信息。这一疏忽限制了它们在识别细微或异质性肿瘤方面的有效性，因为准确检测依赖于更广泛的遗传背景。除了基因组特征外，病理图像也能提供丰富的视觉信息以反映AR的形态。为了利用患者级别的诊断知识并整合组织学图像和ST的互补信息，我们开发了一种bulk RNA-seq引导的多模态异常区域检测方法（BRGMAR），用于从人体组织中识别AR。具体而言，为了有效建模ST中的依赖关系，我们引入了一个动态多关系图学习（DMRGL）模块，以自适应地捕获ST中的复杂关系，包括空间邻近性和基因表达相似性。然后，我们设计了一种基于最优传输的基因模块对齐（OTGMA）方法，通过匹配ST数据与患者级别bulk RNA-seq数据相应基因模块的组成和功能相似性，将二者对齐。最后，我们将学习到的基因组特征与病理图像表示相结合，以实现准确的AR检测。我们在三个公开可用的ST数据集上评估了我们的方法，用于从正常组织中识别癌变区域，实验结果证明了我们的方法相较于现有研究的优势。
英文原文摘要 1791 字符
Spatial transcriptomics (ST) has emerged as a revolutionary approach in the field of tissue analysis that can offer spatial resolved molecular insights for the identification of anomalous regions (AR) on human cancers. Current ST-based methods for detecting AR focus narrowly on the molecular features of local tissue spots, overlooking the matched bulk RNA-seq data that contains crucial diagnostic information. This oversight limits their effectiveness in identifying subtle or heterogeneous tumors, where accurate detection depends on broader genetic context. Besides the genomic signatures, the pathological images can also provide rich visual information to reflect the morphology of AR. To utilize the patient-level diagnostic knowledge and harness complementary information from both histology images and ST, we develop a Bulk RNA-seq Guided Multi-modal Anomalous Regions Detection method (BRGMAR) for the identification of AR from human tissues. Specifically, to effectively model the dependencies in ST, we introduce a Dynamic Multi-Relational Graph Learning (DMRGL) module to adaptively capture complex relationships in ST, including both spatial proximity and gene expression similarity. Then, we design an Optimal Transportation-based Gene Module Alignment (OTGMA) approach to align ST data with patient-level bulk RNA-seq data by matching the compositional and functional similarities of their corresponding gene modules. Finally, we combine the learned genomic features with pathological image representations for accurate AR detection. We evaluate our method on three public available ST datasets for the purpose of identifying cancerous regions from normal tissues, and the experimental results demonstrate the advantage of our method in comparison with the existing studies.