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bioRxiv · 10.64898/2026.04.14.718399

RapCluster: Bridging the Reproducibility Gap in Clustering Analysis

Lutfi, A.·Warneke, R.·Fischer, L.·Rappsilber, J.
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Abstract

Clustering is ubiquitous across science, yet a text-mining audit of 736,399 open-access articles identified as using clustering (2000-2025) reveals common practice leaves key parameters undocumented or untuned, contributing to the reproducibility crisis in science. We developed an interactive web platform featuring 11 widely adopted clustering algorithms to enable transparent clustering analysis and reporting, aligning practical use with best practices in computational research. Code availabilityThe browser-based clustering analysis platform RapCluster is available for download at https://github.com/lutfia95/RapCluster under MIT License and accessible at https://rappsilberlab.org/rapcluster/ The web version is capacity-limited to 8 GB of memory (ca. 12,000 candidates with 130 features). All source codes used for the analysis are included with the publication.

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Lutfi, A., Warneke, R., Fischer, L., Rappsilber, J.. 2026-04-15. RapCluster: Bridging the Reproducibility Gap in Clustering Analysis. https://doi.org/10.64898/2026.04.14.718399

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