The Reproducibility Gap in Graph Neural Network Workflows for Cell Dynamics: A Checklist-Driven Evaluation    

The Reproducibility Gap in Graph Neural Network Workflows for Cell Dynamics: A Checklist-Driven Evaluation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 2 December 2025 V1 Latest version Share on The Reproducibility Gap in Graph Neural Network Workflows for Cell Dynamics: A Checklist-Driven Evaluation Authors : Martin Schätz 0000-0003-0931-4017 [email protected] and Ko Sugawara 0000-0002-1392-9340 Authors Info & Affiliations https://doi.org/10.22541/au.176463842.29639629/v1 214 views 52 downloads Contents Abstract Introduction Target paper overview Aims for Reusing the Results Applied Checklists and Standards Reproduction Protocol Evaluation Findings: The Reproducibility Gap Discussion and Actionable Recommendations Conclusion Acknowledgments Competing Interests Disclosure of AI Use Data Availability Statement Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract As part of a Global BioImage Analysts’ Society (GloBIAS) initiative, we evaluated the reproducibility of a Graph Neural Network (GNN) study on cell dynamics using structured, community-developed checklists from the Quality Assessment and Reproducibility for Instruments and Images in Light Microscopy (QUAREP-LiMi) initiative. Our systematic assessment revealed a substantial gap between minimal reporting standards and practical execution. Reproduction attempts across multiple environments confirmed critical deficiencies, including the absence of explicit image metadata (pixel size and timestamps), which compromised quantitative interpretation. Furthermore, failure to provide a functional software container (Dockerfile) or a complete dependency list necessitated complex manual environment configuration, severely raising the barrier to entry. This experience highlights the critical difference between the illusion of reproducibility achieved by minimal compliance and true functional access. We conclude by presenting a detailed discussion of the broader implications for the quantitative biology community and propose actionable recommendations—including robust environment containerization and standardised data deposition—to ensure complex computational workflows become scientifically sound and reusable resources. Keywords --- Graph Neural Networks, Computational Reproducibility, Bioimage Analysis Standards, Quantitative Microscopy Graph Neural Networks (GNN), Computational Reproducibility,Bioimage Analysis Standards, Quantitative Microscopy Introduction This paper critically assesses the practical reproducibility of a cutting-edge image analysis workflow, published as the study "Probing the rules of cell coordination in live tissues by interpretable machine learning based on graph neural networks" (the Target Paper). This work is essential because computational methods, particularly those involving Graph Neural Networks (GNNs) in quantitative biology, represent a high-stakes domain where reproducibility failures can impede scientific progress. We apply a standardized methodology—derived from community-developed checklists (including those for Image Data, Machine Learning, and Workflow)—to evaluate the practical implementation of the Target Paper's published code and data. Our primary goal is to analyze the gap between stated intentions for reproducibility and the practical, user-facing reality of executing the analysis, thereby providing concrete recommendations to raise the standard of computational rigor in the field. Target paper overview The Target Paper, "Probing the rules of cell coordination in live tissues by interpretable machine learning based on graph neural networks" \cite{Yamamoto_2022} introduces a novel analytical framework for understanding cell fate determination in tissues. Simplified workflow overview. ( a ) Overall workflow steps of the used approach in the target paper. Steps 1 and 2 were not in the scope of the target paper. It was part of previous research. ( b ) Source data overview \cite{Mesa2018-js} , \cite{Cockburn2022-xt} , \cite{Yamamoto_2022} ( c ) ( c ) Figure adapted from \cite{Yamamoto_2022} , licensed under CC-BY 4.0. ( d ) Motivation The stability and robustness of developing and homeostatic tissues depend on various types of spatiotemporal cell-to-cell interactions. Although live imaging and cell tracking offer direct evidence of cell coordination rules, extracting and comparing these rules across diverse tissues—which may operate on different length and timescales of coordination—requires a versatile analytical framework. The research aims to infer cell-to-cell interaction rules from live images of multicellular dynamics, thereby understanding how cell coordination shapes complex developmental outcomes, notably cellular lineage commitment. Existing analyses of skin stem cells, such as those conducted on the mouse hind paw epidermis, were often hypothesis-based and might fail to extract suppressive effects. Therefore, a generic framework capable of suggesting coordination rules in an unbiased manner was necessary. Approach The authors propose using Graph Neural Network (GNN) models because they are well-suited for predicting cell fate and inferring the underlying cell interactions that govern multicellular dynamics. 1. Data Graph Construction: Live-image data and cell tracks (including divisions and delaminations) from the basal layer of the epidermis are converted into spatiotemporal graphs. Nodes represent cells in a specific time frame, and edges encode spatial relationships (cell-to-cell contact) and temporal relationships (lineage tracks). 2. Model Structure: The GNN model, termed the bidirectional spatiotemporal GNN model, processes information through sequential message passing (MP): backward temporal MP (future to past), spatial MP (bidirectionally on contacts), and forward temporal MP (past to future). The model was applied using datasets of live mammalian epidermis (hind paw \cite{Mesa2018-js} and ear skin stem cells \cite{Cockburn2022-xt} ). 3. Interpretability: To extract the rules learned by the machine, the researchers employed the attribution method called Integrated Gradients (IG), which quantifies the impact of each input variable on the output prediction score. Solution The GNN framework ( Fig. 1 . ) proved successful in inferring cell interaction rules without prior knowledge of the signalling mechanisms involved. • Prediction Success: The GNN successfully predicted cell fates (Division, Delamination, or No Behavior (NB)) in the epidermis basal layer. Intriguingly, the AUC scores indicated that the graph structure itself contains useful information for predicting cell fate, even when cell features like area or G1-phase reporter signal were excluded. • Discovery of Rules: The analysis discovered distinct neighbour cell fate coordination rules between different regions of the body (hind paw vs. ear epidermis). • In the hind paw, the model confirmed the previously reported effect of Delamination-induced neighboring Division (with a delayed effect of 1–2 days). It also suggested a potentially novel mechanism where neighbouring Delamination suppresses Delamination in the next frame (lateral inhibition). • In the ear skin data, an additional coordination rule was found, suggesting a mechanism of neighbouring Division-induced Delamination and Division-suppression, which was absent in the hind paw epidermis. • Validation: By applying the attribution method to numerical simulations, the authors confirmed that the GNN accurately extracted the artificially implemented coordination rules (e.g., Delamination-induced Division and Division-induced Delamination). Aims for Reusing the Results The authors aimed to facilitate the reuse and extension of their results by providing resources and proposing broad applicability for the framework: • Data and Code Availability: The paper states that the pipeline of the GNN framework and the analysed data (tracks of cells in the epidermis and numerical simulations) are available on a GitHub repository. • Broad Applicability: The GNN framework is considered applicable not only to the current epidermis data but also for general multicellular dynamics, such as those in developing embryos, malignant tissues, and organoids. • Applicability Beyond Biology: The method is useful for a wide range of systems where modelling by stochastic interacting agents is effective, including disease spreading and ecology. • Methodological Extension: The attribution methods employed are intended to lay the groundwork for developing methods capable of interpreting the effects of higher-order subgraph motifs (e.g., three-body cell interactions or fate imbalance across generations), potentially using techniques like attention-based GNN Methodology Our evaluation is based on a standardized, multi-pronged protocol designed to test the full spectrum of reproducibility, from data reporting to code execution. Applied Checklists and Standards The assessment was guided by three complementary, community-developed checklists \cite{Schmied_2023} , ensuring a comprehensive critique tailored to the Target Paper's specific nature (quantitative imaging and machine learning). These checklists, created by the ‘Image Analysis and Visualization Working Group’ within the Quality Assessment and Reproducibility for Instruments and Images in Light Microscopy (QUAREP-LiMi) initiative, are designed to enhance the clarity and reproducibility of light microscopy images and their analyses in publications. Our approach consists of systematical evaluation of the selected published paper and data against the three defined reporting levels: ‘minimal’ (necessary requirements), ‘recommended’ (measures for understandability), and ‘ideal’ (future considerations). This practical application of the checklists will allow for a comprehensive evaluation of how well published image figures and image-analysis workflows adhere to established standards for reproducibility. The following checklists will be used: 1. Checklist for image publication provides key recommendations for preparing light microscopy images for publication to enhance their clarity and interpretability. It covers critical aspects such as Image Format (e.g., appropriate cropping, rotation, separation of individual images, showing example images for quantification and phenotype ranges), Image Colours and Channels (e.g., clear annotation of channels, proper brightness/contrast adjustment, high-visibility colours, provision of grayscale versions, and intensity scales), Image Annotation (e.g., adding legible scale information, explaining all annotations, and including important imaging details), and Image Availability (e.g., sharing images, preferably lossless compressed or original files, and making them freely downloadable via public or dedicated image databases). 2. Checklist for publication of image-analysis workflows: These checklists offer structured guidelines for transparently documenting image-processing and analysis steps, which is essential for the reproducibility of results derived from image analysis. They are further divided into three specific categories based on the nature of the workflow: Established Workflows: Focuses on documenting workflows already available in scientific literature or well-established within specific fields, requiring citation of the workflow and platform, reporting of key settings, provision of example input/output data, documentation of any manual interventions (e.g., regions of interest), and precise version numbers of the workflow and software. New Workflows: Addresses novel sequences of established or new image-analysis components, necessitating detailed description and citation of individual components, specification of exact software versions, description of the application sequence, documentation of key settings, and sharing of the developed workflow as code or pipelines along with example data. Machine Learning Workflows: Recognises the unique challenges of machine learning, especially deep learning, requiring citation of the original method, access to the developed model and validation data, clear identification of pretrained models, and for retrained or new models, making full training and testing data, training metadata, and the code used for training available. Reproduction Protocol The Target Paper's published code and data were subjected to a rigorous reproduction attempt across multiple environments to test for environment-agnostic reliability based on the provided instructions. The key steps were: 1. Local Environment Setup: An attempt was made to build the environment locally using the documentation provided, revealing essential, unlisted dependencies (e.g., pyyaml, tifffile) and mandatory Python (3.7) and CUDA (10.1) version downgrades. 2. Containerization Test: The search for a functional Dockerfile or robust environment configuration file was conducted to assess adherence to the "ideal" standard of portable, containerized execution. 3. Cloud Environment Test (Google Colab): An attempt was made to execute the workflow within a common, pre-configured cloud platform (Google Colab) to specifically assess the barrier to entry for users without high-end local hardware or complex setup knowledge. 4. Execution and Verification: All primary analysis scripts (e.g., "run-functions") were executed. The assessment focused on the success rate, the clarity of diagnostic output, and the ease of verifying that the generated outputs matched the Target Paper's reported results. Evaluation Findings: The Reproducibility Gap The application of the standardised methodology revealed a significant disparity between the Target Paper's stated commitment to reproducibility and the practical experience of executing the workflow. While the authors met several "minimal" requirements for sharing data and code, key deficiencies in documentation, metadata, and environment specification created a substantial reproducibility gap. Data Availability and Metadata Compliance The authors successfully met the minimal requirement of the Machine Learning Workflow Checklist by providing segmented and tracked data in .NPY files, alongside dedicated example training and testing sets for both synthetic and real data. Code and data were publicly shared on a GitHub repository. The analysis utilises data stored in .NPY files, specifically a segmentation image (segmentation.npy) and track data (lineage.npy). The segmentation.npy files are described as NumPy arrays with dimensions [T,H,W] for image sequences, where cell borders are set to 0. The lineage.npy files are NumPy arrays containing 10 columns, including Timeframe, CellID, PositionX, and PositionY. The project provides two sets of examples for synthetic data (data/simulation/del-div/sample0 for training and sample1 for testing) and two sets for real sample segmentation (data/epidermis/paw/W-R1 for training and W-R2 for testing). This provision of validation input and output data, including both training and testing subsets, meets the 'minimal' requirement for Machine Learning Workflows and the requirement for example input and output data for New Workflows. The evaluation identified three compliance issues: 1. Non-Standard Format: The reliance on the proprietary .NPY format for the image-derived data fails to adhere to the "ideal" standard of the Image Publication Checklist, which recommends formats like OME-TIFF to retain rich, structured image metadata. 2. Missing Crucial Metadata: A fundamental compliance failure was the probable absence of crucial metadata, particularly explicit pixel size and timestamps (μm/pixel, imaging intervals). The explicit provision of scale information is a minimal requirement for published image analysis, as its absence renders any quantitative metric derived from the images (e.g., velocities, distances) unverifiable and non-reproducible. While the text of the paper mentions a region size, and all images have the same size, this information is not embedded in the data. 3. Lack of Archival Persistence: While GitHub is an accessible platform, the lack of an archived repository with a Digital Object Identifier (DOI) (e.g., via Zenodo) falls short of the recommended standards for long-term data preservation, versioning and citability. Code Quality and Workflow Execution The practical execution of the workflow was significantly constrained by code structure and documentation deficiencies, preventing the achievement of the "recommended" and "ideal" standards of the New Workflow Checklist. 1. Library Documentation: The README meets the 'minimal' requirement by listing precise software versions for pytorch (1.6.0), python (3.7.7), dgl (0.5.0), numpy (1.18.5), matplotlib (3.3.1), and scikit-learn (0.23.2). Furthermore, the stated use of a Docker container (pytorch:1.6.0-cuda10.1-cudnn7-runtime) aims for the 'recommended' to 'ideal' standard of providing workflows packaged in software containers to minimise environmental variations. 2. Code Structure and Navigation: The code is not structured as a standard Python package, and key operations are performed by "run-functions" nested in subfolders that rely on hardcoded, relative paths. This structure fundamentally complicates code navigation, isolating specific components, and reusing individual functions. 3. Documentation Deficiencies: The code base mostly lacks standard docstrings to explain the function, input, and output of major scripts. Furthermore, while the general sequence of steps is described, the documentation fails to include a detailed discussion of the rationale, assumptions, and limitations for each step/component, a key element of the "recommended" standard. 4. Verification Failure Despite Successful Run: Crucially, while the code executed to completion without producing runtime errors (a positive finding), the scripts provided very little diagnostic output or clear information regarding the generated results. Multiple files and folders were generated in the indicated results directory, but due to the lack of documentation on the expected file names, format, and content of the final outputs, we were unable to confirm the completeness or scientific fidelity of the results. This low feedback severely compromises the final step of verification. Environment Setup and Barrier to Entry The most significant barrier to reproducibility was encountered during the environment setup, which demanded substantial, undocumented manual intervention, raising the barrier to entry from a minor hurdle to a complex programming task. 1. Failure of Containerization: Although the project intended to meet the "ideal" standard of environmental transparency via containerization, a functional Dockerfile was missing from the repository, rendering the specific build environment non-transparent and effectively making it impossible to rebuild reliably. Only Docker container was specified: pytorch:1.6.0-cuda10.1-cudnn7-runtime. 2. Mandatory Undocumented Libraries: Any setup required manual installation of unwritten dependencies (e.g., pyyaml (6.0.1), tifffile (2021.11.2)). This dependency management failure is directly linked to the absence of standard installation files like requirements.txt or setup.py. 3. Cloud Environment Status: The attempt to run the workflow on a pre-configured cloud environment (Google Colab) confirmed the difficulties presented by the missing environment files, requiring the same time-consuming and error-prone manual setup process, which suggests that the workflow is not robustly portable. While it was possible to run the code after manually downgrading the core Python version and installing specific packages, it would not be considered an entry-level task for a biologist. The issues of environment setup, metadata adherence, and workflow verification encountered with the Target Paper are not unique, highlighting a widespread need for robust, community-supported computational solutions in quantitative biology. To guide researchers toward better practices and demonstrate existing, high-standard implementations, Table 1 provides an overview of several alternative or complementary software. Discussion and Actionable Recommendations The evaluation confirms that while the Target Paper contributes a novel Graph Neural Network approach to a complex problem in quantitative biology, its utility for the wider scientific community is greatly obstructed by deficiencies in practical reproducibility. The core issue is the systemic failure to translate the discussed and explained content of the paper into a functional, user-friendly, and portable computational workflow. This section outlines the broader implications of these findings for the quantitative biology community and provides a dedicated, prioritized action plan for the original authors. Broader Implications for Quantitative Biology The findings regarding the Target Paper serve as a critical case study illustrating common pitfalls in publishing high-end computational biology: • The Illusion of Reproducibility: The paper's public release of code and data created an illusion of reproducibility. Our experience demonstrates that compliance with "minimal" requirements (e.g., uploading files) is insufficient. True reproducibility requires satisfying "recommended" and "ideal" standards for code quality, environment management, and comprehensive documentation. The critical failure was that execution success without verification documentation is scientifically meaningless. Code running without error only confirms script syntax; the absence of documented, expected outputs prevents the crucial final step of confirming scientific fidelity. The main issue was that, although the execution succeeded, the absence of an output verification process made it difficult to assess its validity. • Barriers to Adoption and Systematic Cost: The need for manual dependency installation and the absence of containerization create a significant barrier to entry. This not only prevents replication but also limits the adoption of the methodology by non-specialist labs or researchers attempting to apply the GNN framework to different datasets. This inefficiency is not isolated; a 2016 Nature survey found that over 70% of scientists struggle to reproduce the experiments of others \cite{Baker_2016} , leading to an estimated annual cost of billions of dollars in lost efficiency in preclinical research alone \cite{Freedman_2015} . • The Role of Checklists: This evaluation highlights the crucial role of community-developed standards (like the checklists used here) in moving beyond general guidelines. They provide a structured framework to objectively measure the quality of a scientific release, fostering a culture where reproducibility is an engineered feature rather than an assumed byproduct. Proposed Remediation Plan This plan provides a structured, prioritized list of actions necessary for the authors of the Target Paper to transition their project from a non-reproducible codebase to a scientifically robust, community-ready resource. I. Immediate / Minimal Requirement Fixes These issues violate basic standards and must be addressed immediately to ensure data integrity and basic execution: 1. Metadata Correction: Add explicit, unambiguous metadata to the repository documentation, specifically documenting the pixel size, scale bar information, and imaging intervals (timestamps) used for the original dataset. 2. Dependency Management: Create and test a standard, machine-readable dependency list, such as a requirements.txt or an environment.yml file, to eliminate the need for manual, trial-and-error dependency installation and system downgrades. 3. Data Preservation: Move the core dataset files from the GitHub repository to a dedicated, public data repository (e.g., BioImageArchive or Zenodo) and obtain a Digital Object Identifier (DOI) for stable, long-term archival and citation. Ideal approach would be to have separate repository for code and for the image data. II. Recommended / Workflow Improvement Fixes These actions address code quality and documentation issues that currently prevent easy use and adaptation: 1. Code Packaging: Refactor the codebase into a standard Python package structure. This will eliminate the reliance on hardcoded paths and nested "run-functions," making individual modules importable and reusable. 2. Inline Documentation: Implement comprehensive docstrings for all major functions and scripts, clearly detailing inputs, outputs, data types, and any necessary preconditions or side effects. 3. Verification Documentation: Create a simple, step-by-step tutorial, preferably in a Jupyter Notebook or detailed README, that includes small sample data and illustrates the entire workflow. This documentation must explicitly detail the expected final output structure, including the names, locations, and contents of the files and folders generated, which is necessary to confirm that the execution was scientifically successful. III. Ideal / Portability and Transparency Fixes These are advanced steps required to meet the highest standards of transparency and environment portability: 1. Container Provisioning: Develop and test a functional Dockerfile (or Singularity/Apptainer container) that successfully builds the complete environment without external manual intervention (including all the correct Python Libraries and CUDA versions), ensuring the workflow can be executed identically by any user. 2. Comprehensive Rationale: Augment the documentation to include a detailed rationale, assumptions, and limitations for each step of the workflow, particularly regarding segmentation, graph construction, and the specific choices made for the GNN architecture. Conclusion The systematic assessment of the Target Paper's GNN workflow highlights a crucial, solvable challenge: transitioning from open data principles to achieved computational reproducibility. As pioneers defined it, the goal is to allow a colleague to "run the same software on the same input data and obtain the same results" \cite{Claerbout_1992} . The failure here—epitomised by missing metadata, a lack of a defined environment, and a critical absence of verification documentation—reinforces that reproducibility must be an engineered feature of the publication process. Unified approach to Improve Research Practices As Miura and Nørrelykke \cite{Miura_2021} argue in their work on reproducible image analysis, establishing a new norm requires a "precise description of the image processing and analysis applied." This necessity demands a unified commitment to improvement (Fig. \ref{929825}) across all stakeholders: • For Authors: The immediate priority is to adopt DevOps practices and literate programming. By utilizing tools like BIAFLOWS \cite{Rubens_2020} , which offers a versatile and extensible integrated framework for workflows, authors can easily meet the "Ideal" standard of environment portability and verification. • For Reviewers and Journals: We must mandate the use of structured, community-developed checklists to verify computational rigor before publication, thereby moving beyond checking for mere data upload. • For the Community: This case is not a critique of individuals, but a call to collective action. By mandating standards like FAIR data principles and rewarding high-quality environment specifications and verification documentation, we can eliminate the costly "forensic research" currently needed to validate published work, ultimately accelerating scientific discovery in quantitative and computational biology. Acknowledgments We thank Takaki Yamamoto and Kyogo Kawaguchi for providing detailed information on the data and methodologies used in this study. We would like to express our sincere gratitude to Nicholas Wedige, Felipe Delestro, and Nicole Salgado, whose experience and contributions were invaluable in testing and gathering supporting information for this work. This project has been made possible in part by grant number 2023-321238 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation. This work was supported by the Vinicna Microscopy Core Facility (RRID:SCR_026602) co-financed by the Czech-BioImaging large RI project LM2023050; Computational resources were supplied by the e-INFRA CZ project (ID:90254) provided within the program Projects of Large Research, Development, and Innovations Infrastructures; Core Research for Evolutionary Science and Technology (CREST) JPMJCR1926. Competing Interests K.S. is employed part-time by LPIXEL Inc. Disclosure of AI Use Artificial intelligence tools were used in the preparation of this manuscript as follows: Google NotebookLM was employed to export, organize and summarize various types of research notes. Grammarly Premium was used to improve grammar and style. All AI-assisted outputs were reviewed and verified by the authors to ensure accuracy, originality, and compliance with Wiley’s ethical and editorial standards. No AI tools were used to generate, alter, or manipulate original research data or results. Data Availability Statement The data that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.17777052 , reference number \cite{takaki_yamamoto_2025_17777052} , under Apache License 2.0 licence. Information & Authors Information Version history V1 Version 1 02 December 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords bioimage analysis standards computational reproducibility graph neural networks quantitative microscopy Authors Affiliations Martin Schätz 0000-0003-0931-4017 [email protected] Department of Mathemtacis Informatics and Cybernetics, University of Chemistry and Technology, Prague, Czech Republic View all articles by this author Ko Sugawara 0000-0002-1392-9340 Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan View all articles by this author Metrics & Citations Metrics Article Usage 214 views 52 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Martin Schätz, Ko Sugawara. The Reproducibility Gap in Graph Neural Network Workflows for Cell Dynamics: A Checklist-Driven Evaluation . Authorea . 02 December 2025. 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