by Mengyi Yan, Xiaoxiu Tan, Zhenhao Liu, Siwen Zhang, Yiwen Du, Hong Li, Lanming Chen, Lu XieSpatial omics enables analysis of tissue microenvironmental heterogeneity, but most spatial tools are optimized for transcriptomics and poorly suited to proteomics data. We developed Sphinx, an R package providing a comprehensive workflow for spatial proteomics, including preprocessing, cell annotation, adaptive spatial neighborhood modeling, and functional characterization. Importantly, it introduces an adaptive parameter selection framework guided by spatial statistical metrics and incorporates computational optimizations for large-scale datasets, enabling robust neighborhood modeling. Notably, Sphinx offers improved computational efficiency and scalability. Across lung adenocarcinoma, colorectal cancer, and mouse spleen datasets, Sphinx robustly identifies cell types and spatial functional units, including tertiary lymphoid structures. We also explored the application of Sphinx to paired high-resolution spatial transcriptomics datasets, illustrating its potential applicability. Sphinx provides a flexible and efficient framework for spatial proteomics, enabling robust characterization of spatial organization and tissue microenvironments.