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bioRxiv · 10.1101/2025.04.06.647437

STEAMBOAT: Attention-based multiscale delineation of cellular interactions in tissues

Liang, S.·Tang, J.·Wang, G.·Ma, J.
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Abstract

Spatial-omics technologies profile cells in their native spatial context within tissues, enabling more complete understanding of cellular properties. However, a key computational challenge remains: identifying cellular interactions that underlie cell types and states - interactions that are essential for spatial organization and provide a biologically grounded framework for understanding cell identities and spatial patterns. These interactions span different distances and thus require multiscale modeling, which remains a major gap in existing methods. Here, we introduce SO_SCPLOWTEAMBOATC_SCPLOW, an interpretable machine learning framework that leverage a self-supervised, multi-head attention model to uniquely decompose gene expression of a cell into multiple key factors: intrinsic cell programs, neighboring cell communication, and long-range interactions. By applying SO_SCPLOWTEAMBOATC_SCPLOW to diverse tissues in health and disease across various spatial-omics technologies, we demonstrate its ability to uncover critical multiscale cellular interactions, capturing classical contact signaling and revealing previously unrecognized patterns of cellular communication. SO_SCPLOWTEAMBOATC_SCPLOW provides a powerful approach for spatial-omics analysis, offering new insights into the multiscale spatial organization of cells and their communication across a wide range of biological contexts.

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Liang, S., Tang, J., Wang, G., Ma, J.. 2025-04-10. STEAMBOAT: Attention-based multiscale delineation of cellular interactions in tissues. https://doi.org/10.1101/2025.04.06.647437

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