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The reproducibility gap in graph neural network workflows for cell dynamics: A checklist-driven case study.

Martin Schätz¹, Ko Sugawara²

¹Department of Mathematics Informatics and Cybernetics, University of Chemistry and Technology, Prague, Czech Republic.

Journal of Microscopy

|June 23, 2026

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Evaluating a Graph Neural Network (GNN) study revealed reproducibility gaps. Missing metadata and complex setups hinder quantitative biology research, highlighting the need for better standards.

Area of Science:

Quantitative Biology
Bioimage Analysis
Computational Microscopy

Background:

The Global BioImage Analysts' Society (GloBIAS) initiative aims to improve reproducibility in biological image analysis.
The Quality Assessment and Reproducibility for Instruments and Images in Light Microscopy (QUAREP-LiMi) initiative provides checklists for assessing reproducibility.
This study retrospectively evaluates a 2022 Graph Neural Network (GNN) paper using QUAREP-LiMi standards.

Purpose of the Study:

To assess the reproducibility of a GNN study on cell dynamics.
To identify gaps between current reporting standards and practical execution of computational workflows.
To extract lessons for establishing future research standards in quantitative biology.

Main Methods:

Utilized structured, community-developed checklists from the QUAREP-LiMi initiative.

Keywords:

bioimage analysis standards computational reproducibility graph neural networks quantitative microscopy

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Performed reproduction attempts of the GNN study across multiple computational environments.

Retrospectively analyzed a legacy computational paper against modern reproducibility frameworks.

Main Results:

Identified significant reproducibility deficiencies in the target GNN study.
Confirmed the absence of crucial image metadata (pixel size, timestamps), limiting quantitative interpretation.
Observed challenges in environment setup due to missing software containers and incomplete dependency lists, increasing complexity.

Conclusions:

A gap exists between contemporary reproducibility expectations and the practical implementation in published research.
Robust environment containerization and standardized data deposition are crucial for ensuring the scientific soundness and reusability of complex computational workflows.
This retrospective analysis provides actionable recommendations for advancing reproducibility standards in quantitative biology.