STAPLE: automating spatial transcriptomics analysis and AI interpretation

bioRxiv (Cold Spring Harbor Laboratory) 2026 Preprint Open access

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Authors: 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
+9 more Luciane Tsukamoto Kagohara, Ashley L Kiemen, Anirban Maitra, Dimitrios N. Sidiropoulos, Wesley Tansey, Laura Wood, Atul Deshpande, Michael Noble, Elana Judith Fertig
DOI: 10.64898/2026.03.30.715127
Cited by: 1
References: 21

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.

Fields of Study & Subjects

Single-cell and spatial transcriptomics Cell Image Analysis Techniques Gene expression and cancer classification

Publication details

Journal / source
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Publication date
1 April 2026
Language
EN
Document type
Preprint