COAST: Context-Aware Differential Learning for Gene Expression Prediction in Spatial Transcriptomics

• URL: http://arxiv.org/abs/2607.09166v1
• Date: Fri, 10 Jul 2026 07:50:23 GMT
• Title: COAST: Context-Aware Differential Learning for Gene Expression Prediction in Spatial Transcriptomics
• Abstract summary: We propose COAST, a context-aware differential learning framework for spatial gene expression prediction.<n> COAST aggregates the target and context spot tokens using a Transformer encoder to capture both fine-grained local patterns and slide-level structure.<n> Experiments on multiple spatial transcriptomics datasets show consistent improvements in correlation- and distribution-based metrics.
• Score: 2.073620179775842
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Spatial transcriptomics enables profiling of spatial gene expression but is limited by high cost and low throughput, motivating prediction from H&E histopathology images. Existing context-aware methods mainly supervise absolute expression, while relative expression relationships between spots are rarely used explicitly. We propose COAST, a context-aware differential learning framework for spatial gene expression prediction. COAST conditions the local and global context features with type-specific modulation and aggregates the target and context spot tokens using a Transformer encoder to capture both fine-grained local patterns and slide-level structure. It is trained with a joint objective that combines absolute expression regression with signed differential regression between the target and context spots. Experiments on multiple spatial transcriptomics datasets show consistent improvements in correlation- and distribution-based metrics, demonstrating the effectiveness of context-aware differential learning for histology-based spatial gene expression prediction.

Related papers

• Program-space Diffusion for Morphology-to-Transcriptomics Prediction [0.0]
  We reformulate morphology-to-transcriptomics prediction as conditional generation in program space.<n>We extract a low-dimensional set of programs capturing coordinated expression variation in the training data.<n>We train a conditional diffusion model to generate program activations from histology.
  arXiv  Detail & Related papers  (2026-08-14T14:20:40Z)
• Cross-Subject Semantic Decoding with Shared-Space Alignment for Generalized Neural Representation Learning [80.18561652862725]
  Generalizing across subjects remains challenging in invasive neural recordings.<n>We propose a cross-subject semantic decoding framework that aligns neural responses to speech perception from multiple subjects into a shared latent space.
  arXiv  Detail & Related papers  (2026-07-03T03:59:16Z)
• Contextualizing Biological Language Models across Modalities via Logit-Space Contrastive Alignment [2.249367818885166]
  LOGICA is a framework for context-conditioned prediction that performs contrastive learning directly in output-logit space.<n>It is particularly effective for mutation-local variant ranking, where comparisons reduce to context-conditioned likelihoods of mutant tokens at perturbed sites.
  arXiv  Detail & Related papers  (2026-06-17T05:30:47Z)
• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• Learning Spatial-Temporal Coherent Correlations for Speech-Preserving Facial Expression Manipulation [75.05465510260944]
  Speech-preserving facial expression manipulation (SPFEM) aims to modify facial emotions while meticulously maintaining the mouth animation associated with spoken content.<n>Current works depend on inaccessible paired training samples for the person, where two aligned frames exhibit the same speech content yet differ in emotional expression, limiting the SPFEM applications in real-world scenarios.<n>We propose a novel spatial-temporal coherent correlation learning (STCCL) algorithm, which models the aforementioned correlations as explicit metrics and integrates the metrics to supervise manipulating facial expression and meanwhile better preserving the facial animation of spoken content.
  arXiv  Detail & Related papers  (2026-04-22T06:20:10Z)
• Adapting a Pre-trained Single-Cell Foundation Model to Spatial Gene Expression Generation from Histology Images [5.072469295071516]
  HINGE (HIstology-coNditioned GEneration) retrofits a pre-trained sc-FM into a conditional expression generator.<n> Evaluated on three ST datasets, ours outperforms state-of-the-art baselines on mean Pearson correlation.
  arXiv  Detail & Related papers  (2026-03-20T08:52:39Z)
• A Large-Scale Benchmark of Cross-Modal Learning for Histology and Gene Expression in Spatial Transcriptomics [8.854289521774483]
  HESCAPE is a benchmark for evaluating cross-modal contrastive pretraining in spatial transcriptomics.<n>Gene models pretrained on spatial transcriptomics data outperform both those trained without spatial data and simple baseline approaches.<n>We identify batch effects as a key factor that interferes with effective cross-modal alignment.
  arXiv  Detail & Related papers  (2025-08-02T21:11:36Z)
• Distance-Preserving Spatial Representations in Genomic Data [0.0]
  The spatial context of single-cell gene expression data is crucial for many downstream analyses, yet often remains inaccessible due to practical and technical limitations.<n>We propose a generic representation learning and transfer learning framework dp-VAE, capable of reconstructing the spatial coordinates associated with the provided gene expression data.
  arXiv  Detail & Related papers  (2024-08-01T21:04:27Z)
• SEPAL: Spatial Gene Expression Prediction from Local Graphs [1.4523812806185954]
  We present SEPAL, a new model for predicting genetic profiles from visual tissue appearance. Our method exploits the biological biases of the problem by directly supervising relative differences with respect to mean expression. We propose a novel benchmark that aims to better define the task by following current best practices in transcriptomics.
  arXiv  Detail & Related papers  (2023-09-02T23:24:02Z)
• GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning [55.79997930181418]
  Generalized Zero-Shot Learning aims to recognize images from both the seen and unseen classes by transferring semantic knowledge from seen to unseen classes. It is a promising solution to take the advantage of generative models to hallucinate realistic unseen samples based on the knowledge learned from the seen classes. We propose a novel flow-based generative framework that consists of multiple conditional affine coupling layers for learning unseen data generation.
  arXiv  Detail & Related papers  (2022-07-05T04:04:37Z)
• Spatial machine-learning model diagnostics: a model-agnostic distance-based approach [91.62936410696409]
  This contribution proposes spatial prediction error profiles (SPEPs) and spatial variable importance profiles (SVIPs) as novel model-agnostic assessment and interpretation tools. The SPEPs and SVIPs of geostatistical methods, linear models, random forest, and hybrid algorithms show striking differences and also relevant similarities. The novel diagnostic tools enrich the toolkit of spatial data science, and may improve ML model interpretation, selection, and design.
  arXiv  Detail & Related papers  (2021-11-13T01:50:36Z)
• HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning [74.76431541169342]
  Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. We propose a novel hierarchical semantic-visual adaptation (HSVA) framework to align semantic and visual domains. Experiments on four benchmark datasets demonstrate HSVA achieves superior performance on both conventional and generalized ZSL.
  arXiv  Detail & Related papers  (2021-09-30T14:27:50Z)
• APo-VAE: Text Generation in Hyperbolic Space [116.11974607497986]
  In this paper, we investigate text generation in a hyperbolic latent space to learn continuous hierarchical representations. An Adrial Poincare Variversaational Autoencoder (APo-VAE) is presented, where both the prior and variational posterior of latent variables are defined over a Poincare ball via wrapped normal distributions. Experiments in language modeling and dialog-response generation tasks demonstrate the winning effectiveness of the proposed APo-VAE model.
  arXiv  Detail & Related papers  (2020-04-30T19:05:41Z)

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.