Spatial transcriptomics (ST) technologies have revolutionized the study of gene expression patterns in tissues by providing multimodality data in transcriptomic, spatial, and morphological, offering opportunities for understanding tissue biology beyond transcriptomics. However, we identify the modality bias phenomenon in ST data species, i.e., the inconsistent contribution of different modalities to the labels leads to a tendency for the analysis methods to retain the information of the dominant modality. How to mitigate the adverse effects of modality bias to satisfy various downstream tasks remains a fundamental challenge. This paper introduces Multiple-modality Structure Transformation, named MuST, a novel methodology to tackle the challenge. MuST integrates the multi-modality information contained in the ST data effectively into a uniform latent space to provide a foundation for all the downstream tasks. It learns intrinsic local structures by topology discovery strategy and topology fusion loss function to solve the inconsistencies among different modalities. Thus, these topology-based and deep learning techniques provide a solid foundation for a variety of analytical tasks while coordinating different modalities. The effectiveness of MuST is assessed by performance metrics and biological significance. The results show that it outperforms existing state-of-the-art methods with clear advantages in the precision of identifying and preserving structures of tissues and biomarkers. MuST offers a versatile toolkit for the intricate analysis of complex biological systems.

翻译：空间转录组学（ST）技术通过提供转录组、空间和形态学等多模态数据，彻底改变了对组织中基因表达模式的研究，为超越转录组学理解组织生物学提供了机遇。然而，我们发现了ST数据物种中的模态偏差现象，即不同模态对标签的贡献不一致，导致分析方法倾向于保留主导模态的信息。如何缓解模态偏差的不利影响以满足各种下游任务，仍然是一个基本挑战。本文提出了名为MuST的多模态结构变换方法（Multiple-modality Structure Transformation），以应对这一挑战。MuST将ST数据中包含的多模态信息有效整合到统一的潜在空间中，为所有下游任务提供基础。它通过拓扑发现策略和拓扑融合损失函数学习内在局部结构，以解决不同模态之间的不一致性。因此，这些基于拓扑和深度学习的技术在协调不同模态的同时，为多种分析任务提供了坚实基础。通过性能指标和生物学意义评估了MuST的有效性。结果表明，它在识别和保留组织及生物标志物结构的精确性方面明显优于现有最先进方法。MuST为复杂生物系统的深入分析提供了一个多功能工具包。