SCR2-ST: Combine Single Cell with Spatial Transcriptomics for Efficient Active Sampling via Reinforcement Learning

• URL: http://arxiv.org/abs/2512.13635v1
• Date: Mon, 15 Dec 2025 18:30:40 GMT
• Title: SCR2-ST: Combine Single Cell with Spatial Transcriptomics for Efficient Active Sampling via Reinforcement Learning
• Authors: Junchao Zhu, Ruining Deng, Junlin Guo, Tianyuan Yao, Chongyu Qu, Juming Xiong, Siqi Lu, Zhengyi Lu, Yanfan Zhu, Marilyn Lionts, Yuechen Yang, Yalin Zheng, Yu Wang, Shilin Zhao, Haichun Yang, Yuankai Huo,
• Abstract summary: We introduce SCR2-ST, a unified framework that leverages single-cell prior knowledge to guide efficient data acquisition and accurate expression prediction.<n> SCR2-ST integrates a single-cell guided reinforcement learning-based active sampling and a hybrid regression-retrieval prediction network SCR2Net.
• Score: 9.512448078850392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial transcriptomics (ST) is an emerging technology that enables researchers to investigate the molecular relationships underlying tissue morphology. However, acquiring ST data remains prohibitively expensive, and traditional fixed-grid sampling strategies lead to redundant measurements of morphologically similar or biologically uninformative regions, thus resulting in scarce data that constrain current methods. The well-established single-cell sequencing field, however, could provide rich biological data as an effective auxiliary source to mitigate this limitation. To bridge these gaps, we introduce SCR2-ST, a unified framework that leverages single-cell prior knowledge to guide efficient data acquisition and accurate expression prediction. SCR2-ST integrates a single-cell guided reinforcement learning-based (SCRL) active sampling and a hybrid regression-retrieval prediction network SCR2Net. SCRL combines single-cell foundation model embeddings with spatial density information to construct biologically grounded reward signals, enabling selective acquisition of informative tissue regions under constrained sequencing budgets. SCR2Net then leverages the actively sampled data through a hybrid architecture combining regression-based modeling with retrieval-augmented inference, where a majority cell-type filtering mechanism suppresses noisy matches and retrieved expression profiles serve as soft labels for auxiliary supervision. We evaluated SCR2-ST on three public ST datasets, demonstrating SOTA performance in both sampling efficiency and prediction accuracy, particularly under low-budget scenarios. Code is publicly available at: https://github.com/hrlblab/SCR2ST

Related papers

• Generative MIMO Beam Map Construction for Location Recovery and Beam Tracking [67.65578956523403]
  This paper proposes a generative framework to recover location labels directly from sparse channel state information (CSI) measurements.<n>Instead of directly storing raw CSI, we learn a compact low-dimensional radio map embedding and leverage a generative model to reconstruct the high-dimensional CSI.<n> Numerical experiments demonstrate that the proposed model can improve localization accuracy by over 30% and achieve a 20% capacity gain in non-line-of-sight (NLOS) scenarios.
  arXiv  Detail & Related papers  (2025-11-21T07:25:49Z)
• A Hybrid Computational Intelligence Framework for scRNA-seq Imputation: Integrating scRecover and Random Forests [1.3265006188260802]
  Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at cellular resolution.<n>We present SCR-MF, a modular two-stage workflow that combines principled dropout detection using scRecover with robust non-parametric imputation via missForest.
  arXiv  Detail & Related papers  (2025-11-21T03:23:47Z)
• Generalizable Blood Cell Detection via Unified Dataset and Faster R-CNN [0.33842793760651557]
  This paper presents a comprehensive methodology and comparative performance analysis for the automated classification and object detection of peripheral blood cells.<n>Data pipeline was first developed to standardize and merge four public datasets into a unified resource.<n>State-of-the-art Faster R-CNN object detection framework was employed, leveraging a ResNet-50-FPN backbone.
  arXiv  Detail & Related papers  (2025-11-11T17:08:40Z)
• scMRDR: A scalable and flexible framework for unpaired single-cell multi-omics data integration [53.683726781791385]
  We introduce a scalable and flexible generative framework called single-cell Multi-omics Regularized Disentangled Representations (scMRDR) for unpaired multi-omics integration.<n>Our method achieves excellent performance on benchmark datasets in terms of batch correction, modality alignment, and biological signal preservation.
  arXiv  Detail & Related papers  (2025-10-28T21:28:39Z)
• CellPainTR: Generalizable Representation Learning for Cross-Dataset Cell Painting Analysis [51.56484100374058]
  We introduce CellPainTR, a Transformer-based architecture designed to learn foundational representations of cellular morphology.<n>Our work represents a significant step towards creating truly foundational models for image-based profiling, enabling more reliable and scalable cross-study biological analysis.
  arXiv  Detail & Related papers  (2025-09-02T03:30:07Z)
• Sparser2Sparse: Single-shot Sparser-to-Sparse Learning for Spatial Transcriptomics Imputation with Natural Image Co-learning [1.7603474309877931]
  We present Single-shot Sparser-to-Sparse (S2S-ST), a novel framework for accurate spatial transcriptomics imputation.<n>Our framework requires only a single and low-cost sparsely sampled ST dataset alongside widely available natural images for co-training.
  arXiv  Detail & Related papers  (2025-07-22T17:58:38Z)
• Unlasting: Unpaired Single-Cell Multi-Perturbation Estimation by Dual Conditional Diffusion Implicit Bridges [68.98973318553983]
  We propose a framework based on Dual Diffusion Implicit Bridges (DDIB) to learn the mapping between different data distributions.<n>We integrate gene regulatory network (GRN) information to propagate perturbation signals in a biologically meaningful way.<n>We also incorporate a masking mechanism to predict silent genes, improving the quality of generated profiles.
  arXiv  Detail & Related papers  (2025-06-26T09:05:38Z)
• Private Training & Data Generation by Clustering Embeddings [74.00687214400021]
  Differential privacy (DP) provides a robust framework for protecting individual data.<n>We introduce a novel principled method for DP synthetic image embedding generation.<n> Empirically, a simple two-layer neural network trained on synthetically generated embeddings achieves state-of-the-art (SOTA) classification accuracy.
  arXiv  Detail & Related papers  (2025-06-20T00:17:14Z)
• Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning [53.527506374566485]
  We propose a novel Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning cluster framework, namely AR-DBSCAN.<n>We show that AR-DBSCAN not only improves clustering accuracy by up to 144.1% and 175.3% in the NMI and ARI metrics, respectively, but also is capable of robustly finding dominant parameters.
  arXiv  Detail & Related papers  (2025-05-07T11:37:23Z)
• SUICA: Learning Super-high Dimensional Sparse Implicit Neural Representations for Spatial Transcriptomics [23.396154129613528]
  Spatial Transcriptomics (ST) is a method that captures gene expression profiles aligned with spatial coordinates.<n>In this paper, we manage to model ST in a continuous and compact manner by the proposed tool, SUICA.<n>We incorporate a graph-augmented Autoencoder to effectively model the context information of the unstructured spots.
  arXiv  Detail & Related papers  (2024-12-02T05:02:18Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Reads2Vec: Efficient Embedding of Raw High-Throughput Sequencing Reads Data [2.362412515574206]
  We propose Reads2Vec, an alignment-free embedding approach that can generate a fixed-length feature vector representation directly from raw sequencing reads without requiring assembly. Experiments on simulated data show that our proposed embedding obtains better classification results and better clustering properties contrary to existing alignment-free baselines.
  arXiv  Detail & Related papers  (2022-11-15T16:19:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.