The abstract is read at the publisher; the summary is JClub's.
Sphinx, an R package for spatial proteomics, robustly identifies cell types and spatial functional units, including tertiary lymphoid structures, across lung adenocarcinoma, colorectal cancer, and mouse spleen datasets.
- Why it matters: Spatial omics can capture tissue microenvironment heterogeneity, but many tools are tuned for transcriptomics and do not fit proteomics well. This limits reliable neighborhood modeling and functional characterization in spatial proteomics.
- What they did: Sphinx implements a full workflow—preprocessing, cell annotation, adaptive spatial neighborhood modeling, and functional analysis—using an adaptive parameter selection framework guided by spatial statistical metrics plus computational optimizations for large-scale datasets.
- The result: The package improves computational efficiency and scalability while enabling robust neighborhood modeling and spatial organization characterization, and it also shows potential on paired high-resolution spatial transcriptomics datasets.
The findingWhy it mattersWhat they didThe result
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