Abstract Background: Spatial multi-omics has advanced rapidly in resolution, multiplexing, 3D modeling, and multi-modality. Platforms rely on distinct chemistries and assay architectures, yielding heterogeneous outputs but complementary strengths. These spatially indexed readouts enable neighborhood/niche inference, cell-cell communication, and 3D reconstruction, but demand rigorous normalization, uncertainty modeling, and scalable computation. Despite progress with foundation models for imputation and integration, a domain-specific suite that unifies these models for spatial omics is lacking. Methods: We developed Spyrrow (Spatially Resolved Multi-omics Data Visualization and Analytic Framework), a Python toolkit for cross-platform analysis, visualization, multi-modality integration and enhancement, spatial registration, and 3D reconstruction (https://github.com/WangLab-ComputationalBiology/spyrrow). Spyrrow ingests heterogeneous outputs and standardizes them into a unified spatial data model. Modules span raw-data processing (cell segmentation to cell-level matrices; single-cell enhancement for Visium; Visium HD single-cell transformation using registered H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5505.
Liu et al. (Fri,) studied this question.