# STRATA: Reinforcement-driven Active Sampling with Tri-paradigm Adaptation for Efficient 3D Spatial Transcriptomics

**Authors:** Junchao Zhu, Ruining Deng, Chongyu Qu, Zhengyi Lu, Yanfan Zhu, Yuechen Yang, Yu Wang, Shilin Zhao, Haichun Yang, Yuankai Huo  
**Lab:** Vanderbilt University · **Published:** 29 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.39151) · [PDF](https://arxiv.org/pdf/2610.39151) · [Code](https://github.com/Junchao-Zhu/STRATA) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.39151.html)

## Abstract

Spatial transcriptomics (ST) enables joint characterization of tissue morphology and gene expression. However, 3D volumetric ST acquisition remains expensive. AI-driven methods offer a low-cost alternative but remain limited in accuracy, while recent 2D section-wise ST acquisition still rely on dense grid sampling, resulting in redundant measurements and undersampling of biologically informative regions. To bridge the gap, we propose STRATA, a reinforcement-driven active sampling framework with tri-paradigm adaptation for reconstructing dense 3D transcriptomic maps from sparse voxel-wise ST acquisition. STRATA constructs a 3D graph integrating H&E morphology with single-cell priors and employs a reinforcement learning agent to estimate morphological sparsity and transcriptomic informativeness, enabling budget-aware selection of informative spots. Given these sparse measurements, an adaptive tri-paradigm predictor combines retrieval, regression, and cross-layer imputation through lightweight fusion, dynamically weighting their contributions. Experiments across datasets demonstrate improved sampling efficiency and accuracy, particularly under low-budget settings. Code is available at https://github.com/Junchao-Zhu/STRATA.

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