Must: Maximizing Latent Capacity of Spatial Transcriptomics Data
CoRR(2024)
摘要
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.
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