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.

Spatial transcriptomics 技术在组织中提供了多模态的转录组、空间和形态学数据，为了解转录组以外的组织生物学提供了机会。然而，我们发现 ST 数据种类中存在模态偏差现象，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。本文介绍了一种名为 MuST 的多模态结构转换方法，它有效地将 ST 数据中包含的多模态信息整合到均匀的潜空间，为所有下游任务提供基础。通过拓扑发现策略和拓扑融合损失函数学习内在的局部结构，解决不同模态之间的不一致性。因此，这些基于拓扑的和深度学习技术为各种分析任务提供了坚实的基础，协调了不同的模态。MuST 的有效性通过性能评估和生物学意义进行了评估。结果表明，它在识别和保留组织和生物标志物结构的准确性方面优于现有的最先进方法。MuST 为复杂生物系统的精细分析提供了多功能工具包。