STAPLE: automating spatial transcriptomics analysis and AI interpretation

Dmitrijs Lvovs; Jeffrey Quinn; André Forjaz; Ivette Santana-Cruz; Orian Stapleton; Kranthi Vavikolanu; Meredith Wetzel; Demystifying Pancreatic Cancer Therapies TeamLab; Data Science Teamlab; Vincent Bernard; Brian R. Herb; Alexander V. Favorov; Luciane Tsukamoto Kagohara; Ashley L Kiemen; Anirban Maitra; Dimitrios N. Sidiropoulos; Wesley Tansey; Laura DeLong Wood; Atul Deshpande; Michael S. Noble; Elana Judith Fertig

doi:10.64898/2026.03.30.715127

BioinformaticsbioRxivApr 1, 2026

Preprint

STAPLE: automating spatial transcriptomics analysis and AI interpretation

Dmitrijs Lvovs iD, Jeffrey Quinn iD, André Forjaz iD, Ivette Santana-Cruz iD, Orian Stapleton, Kranthi Vavikolanu, Meredith Wetzel iD, Demystifying Pancreatic Cancer Therapies TeamLab, Data Science Teamlab, Vincent Bernard iD, Brian R. Herb iD, Alexander V. Favorov iD, Luciane Tsukamoto Kagohara iD, Ashley L Kiemen iD, Anirban Maitra iD, Dimitrios N. Sidiropoulos iD, Wesley Tansey iD, Laura DeLong Wood iD, Atul Deshpande iD, Michael S. Noble iD, Elana Judith Fertig iD

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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