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Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering

Research Bioinformatics AI

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TL;DR - SACCELERATOR is an open-source, extensible framework for evaluating spatially aware clustering through a formalized expert-guided consensus process. It aims to address the difficulty of benchmarking spatial clustering when purely quantitative metrics may not capture expert judgments.

  • Formalizes expert input in spatial clustering analysis.
  • Uses a consensus-based approach that goes beyond conventional benchmark scores.
  • Is designed as an extensible, open-source framework.
  • The provided summary does not report specific datasets, algorithms, or performance results.

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Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering

Nature Methods Jieran Sun, Kirti Biharie, Peiying Cai, Niklas Müller-Bötticher, Paul Kiessling, Meghan A. Turner, Søren Helweg Dam, Florian Heyl, Sarusan Kathirchelvan, Martin Emons, Samuel Gunz, Sven Twardziok, Amin El-Heliebi, Martin Zacharias, Søren Helweg Dam, Fadhl Alakwaa, Shahul Alam, María Calleja, Yuzhou Chang, Thomas Chartrand, Nigel S. Chou, Estella Y. Dong, Michael Fletcher, George Gavriilidis, Alexander Kanitz, Sameesh Kher, Louis Kümmerle, Francesca A. Luongo, Qirong Mao, Giorgia Moranzoni, Mar M. Moreno, Anastasiia Okhtienko, Lena Perry, Lucie Pfeiferova, Daryna Pikulska, Shyam Prabhakar, Rasool Saghaleyni, Zaira Seferbekova, Vipul Singhal, Divya Sitani, Charlotte Soneson, Sebastian Tiesmeyer, Marco Varrone, Siao-Han Wong, Liya Zaygerman, Teresa Zulueta-Coarasa, Roland Eils, Marcel Reinders, Raphael Gottardo, Christoph Kuppe, Brian Long, Ahmed Mahfouz, Mark D. Robinson, Naveed Ishaque 2026-08-24 doi:10.1038/s41592-026-03194-8
Public signals OpenAlex citations 4
Providers: Hugging Face · N/A OpenAlex · Citations 4 Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-22 14:32:56.850726 UTC

TL;DR - SACCELERATOR is an open-source, extensible framework for evaluating spatially aware clustering through a formalized expert-guided consensus process. It aims to address the difficulty of benchmarking spatial clustering when purely quantitative metrics may not capture expert judgments.

  • Formalizes expert input in spatial clustering analysis.
  • Uses a consensus-based approach that goes beyond conventional benchmark scores.
  • Is designed as an extensible, open-source framework.
  • The provided summary does not report specific datasets, algorithms, or performance results.
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