SNR-ST-Mix: Sample-specific Neighborhood Regression Mixup for Augmented Spatial Transcriptomics Imputation with Deep Neural Network

• URL: http://arxiv.org/abs/2606.08712v1
• Date: Sun, 07 Jun 2026 16:07:51 GMT
• Title: SNR-ST-Mix: Sample-specific Neighborhood Regression Mixup for Augmented Spatial Transcriptomics Imputation with Deep Neural Network
• Abstract summary: We propose SNR-ST-Mix, a geometry- and expression-aware data augmentation framework for spatial transcriptomics data.<n>It constrains mixing to a spot's k-nearest spatial neighbors and adaptively enables weights based on expression similarity.<n>It consistently outperforms conventional augmentation methods without requiring architectural changes or additional computation.
• Score: 3.6332654414380214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: Spatial transcriptomics (ST) enables gene expression measurements within the tissue context. However, these measurements are often noisy, low-resolution, and sparsely sampled, which limits the recovery of fine spatial structure. Deep neural networks have become powerful tools for expression imputation from histology, but their performance remains constrained by limited sample sizes and a lack of biologically informed augmentation. Most of the existing augmentation strategies for learning are designed for classification tasks rather than regression, which neglect spatial and transcriptomic relationships, leading to biologically implausible interpolations that hinder prediction performance. Approach: To address these limitations, we propose SNR-ST-Mix, a geometry- and expression-aware data augmentation framework designed specifically for ST data. It constrains mixing to a spot's k-nearest spatial neighbors and adaptively weights interpolation coefficients based on expression similarity, generating augmented samples that preserve local biological structure while ensuring spatial smoothness. This dual conditioning yields synthetic examples that expand the effective training manifold, promote generalization, and enhance prediction stability under sample-specific training. Results: Extensive experiments with various tissue types demonstrate that SNR-ST-Mix consistently outperforms conventional augmentation methods without requiring architectural changes or additional computation. Conclusions: SNR-ST-Mix provides an effective and biologically principled augmentation strategy for spatial transcriptomics regression tasks. By explicitly leveraging spatial geometry and transcriptomic similarity, it expands the effective training manifold and improves predictive performance without increasing model complexity.

Related papers

• Test-time Adaptation of Pelvic Bone Segmentation Models via Dynamic Reliability-Guided [62.10276009853225]
  Reliable pelvic bone segmentation from CT is essential for robot-assisted pelvic trauma surgery.<n>Existing methods show limited effectiveness for PBS.<n>We propose a novel closed-loop dynamic Reliability-Guided framework (ReGA) for PBS.
  arXiv  Detail & Related papers  (2026-08-01T08:13:20Z)
• Generative Augmentation of Raman Spectra for Glioma Classification [0.0]
  We develop a conditional variational autoencoder capable of generating class-conditioned synthetic Raman spectra.<n>The generated data are evaluated in Train-on-Synthetic, Test-on-Real (TS/TR) and Train-on-Synthetic+Real, Test-on-Real (TSR/TR) settings.<n> augmenting the real training data with synthetic spectra consistently improves classification performance across multiple models.
  arXiv  Detail & Related papers  (2026-07-11T08:19:51Z)
• Structure-Guided Adaptive Propagation for Protein-Protein Interaction Site Prediction [6.128739346306066]
  We present SGAP-PPIS, a structure-guided adaptive propagation model for PPIS prediction.<n>We show that SGAP-PPIS achieves competitive performance among the state-of-the-art methods on Test_60.
  arXiv  Detail & Related papers  (2026-06-01T07:03:00Z)
• Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback [56.69850045068714]
  We introduce textitInfluence-Guided Regression (IGSR), a method that frames equation discovery as an iterative two-step process.<n>We demonstrate IGSRs effectiveness on a diverse suite of benchmarks, including LLM-SRBench, pharmacological PKPD models, an epidemiological simulation, and real-world genomic data.
  arXiv  Detail & Related papers  (2026-05-27T23:48:01Z)
• GTS: Inference-Time Scaling of Latent Reasoning with a Learnable Gaussian Thought Sampler [54.10960908347221]
  We model latent thought exploration as conditional sampling from learnable densities and instantiate this idea as a Gaussian Thought Sampler (GTS)<n>GTS predicts context-dependent perturbation distributions over continuous reasoning states and is trained with GRPO-style policy optimization while keeping the backbone frozen.
  arXiv  Detail & Related papers  (2026-02-15T09:57:47Z)
• Renormalization Group Guided Tensor Network Structure Search [58.0378300612202]
  Network structure search (TN-SS) aims to automatically discover optimal network topologies and rank robustness for efficient tensor decomposition in high-dimensional data representation.<n>We propose RGTN (Renormalization Group guided Network search), a physics-inspired framework transforming TN-SS via multi-scale renormalization group flows.
  arXiv  Detail & Related papers  (2025-12-31T06:31:43Z)
• Improving Deepfake Detection with Reinforcement Learning-Based Adaptive Data Augmentation [60.04281435591454]
  CRDA (Curriculum Reinforcement-Learning Data Augmentation) is a novel framework guiding detectors to progressively master multi-domain forgery features.<n>Central to our approach is integrating reinforcement learning and causal inference.<n>Our method significantly improves detector generalizability, outperforming SOTA methods across multiple cross-domain datasets.
  arXiv  Detail & Related papers  (2025-11-10T12:45:52Z)
• SDTN and TRN: Adaptive Spectral-Spatial Feature Extraction for Hyperspectral Image Classification [1.2871580250533408]
  Hyperspectral image classification plays a pivotal role in precision agriculture, providing accurate insights into crop health monitoring, disease detection, and soil analysis.<n>Traditional methods struggle with high-dimensional data, spectral-spatial redundancy, and the scarcity of labeled samples, often leading to suboptimal performance.<n>To address these challenges, we propose the Self-Adaptive- Regularized Network (SDTN), which combines tensor decomposition with regularization mechanisms to dynamically adjust tensor ranks.<n>This approach not only maintains high classification accuracy but also significantly reduces computational complexity, making the framework highly suitable for real-time deployment in resource-constrained environments.
  arXiv  Detail & Related papers  (2025-07-13T04:53:33Z)
• A Study of Hybrid and Evolutionary Metaheuristics for Single Hidden Layer Feedforward Neural Network Architecture [1.024113475677323]
  Training Artificial Neural Networks (ANNs) with Gradient Descent (SGD) frequently difficulties encounters.<n>This work investigates Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs)<n>A hybrid PSO-SGD strategy is developed to improve local search efficiency.
  arXiv  Detail & Related papers  (2025-06-17T04:12:58Z)
• High-Fidelity Scientific Simulation Surrogates via Adaptive Implicit Neural Representations [51.90920900332569]
  Implicit neural representations (INRs) offer a compact and continuous framework for modeling spatially structured data.<n>Recent approaches address this by introducing additional features along rigid geometric structures.<n>We propose a simple yet effective alternative: Feature-Adaptive INR (FA-INR)
  arXiv  Detail & Related papers  (2025-06-07T16:45:17Z)
• 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)
• Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data [125.7135706352493]
  Generative adversarial networks (GANs) typically require ample data for training in order to synthesize high-fidelity images. Recent studies have shown that training GANs with limited data remains formidable due to discriminator overfitting. This paper introduces a novel strategy called Adaptive Pseudo Augmentation (APA) to encourage healthy competition between the generator and the discriminator.
  arXiv  Detail & Related papers  (2021-11-12T18:13:45Z)

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.