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Nature Methods
Bioinformatics AI
Jieran Sun, Kirti Biharie, Peiying Cai, Niklas MĂĽller-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, MarĂa Calleja, Yuzhou Chang, Thomas Chartrand, Nigel S. Chou, Estella Y. Dong, Michael Fletcher, George Gavriilidis, Alexander Kanitz, Sameesh Kher, Louis KĂĽmmerle, 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
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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