arXiv Machine Learning September 24, 2026 By Zheyu Zhu, Junchao Zhu, Fengbei Liu, Tianyuan Yao, Gelei Xu, John Cannon, Haichun Yang, Yuankai Huo, Mert R. Sabuncu, Ruining Deng

Benchmarking Active Spot Selection for Cost-Efficient Spatial Transcriptomics

Read the original on arXiv Machine Learning →

The study benchmarks active spot selection methods against random sampling for spatial transcriptomics, focusing on cost‑efficient data acquisition. Using two public cohorts, the authors simulate multi‑round selection with uncertainty‑based (MC‑dropout, TOD) and diversity‑based (CoreSet, TypiClust) strategies, evaluating performance at 5%, 10%, 30%, and 50% of the spot pool. Results show that none of the active strategies consistently outperforms random sampling across all budgets or evaluation metrics, with performance varying by dataset and metric.

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