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STAPLE: automating spatial transcriptomics analysis and AI interpretation

Lvovs, D.; Quinn, J.; Forjaz, A.; Santana-Cruz, I.; Stapleton, O.; Vavikolanu, K.; Wetzel, M.; Data Science Hub TeamLab, ; Demystifying Pancreatic Cancer Therapies TeamLab, ; Pagan, V. B.; Herb, B. R.; Favorov, A.; Kagohara, L. T.; Kiemen, A. L.; Maitra, A.; Sidiropoulos, D. N.; Tansey, W.; Wood, L.; Deshpande, A.; Noble, M.; Fertig, E. J.

2026-04-01 bioinformatics
10.64898/2026.03.30.715127 bioRxiv
Show abstract

Spatial transcriptomics workflows often span separate tools for cell typing, neighborhoods, and cell-cell communication, yielding fragmented outputs that hinder scalability, interpretation, and reproducibility. STAPLE systematizes analyses across distinct methods into a modular framework, unifying data structures and cross-tool interoperability. End-to-end analyses are performed unassisted with a single invocation, fostering rigorous, reproducible spatial transcriptomics analysis. Its novel, AI-enabled reporting layer synthesizes quantitative results into summaries of biological findings, facilitating analysis interpretation.

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