Spatially Gene Expression Prediction using Dual-Scale Contrastive Learning

• URL: http://arxiv.org/abs/2506.23827v1
• Date: Mon, 30 Jun 2025 13:18:39 GMT
• Title: Spatially Gene Expression Prediction using Dual-Scale Contrastive Learning
• Authors: Mingcheng Qu, Yuncong Wu, Donglin Di, Yue Gao, Tonghua Su, Yang Song, Lei Fan,
• Abstract summary: NH2ST integrates spatial context and both pathology and gene modalities for gene expression prediction.<n>Our model consistently outperforms existing methods, achieving over 20% in PCC metrics.
• Score: 12.35331063443348
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial transcriptomics (ST) provides crucial insights into tissue micro-environments, but is limited to its high cost and complexity. As an alternative, predicting gene expression from pathology whole slide images (WSI) is gaining increasing attention. However, existing methods typically rely on single patches or a single pathology modality, neglecting the complex spatial and molecular interactions between target and neighboring information (e.g., gene co-expression). This leads to a failure in establishing connections among adjacent regions and capturing intricate cross-modal relationships. To address these issues, we propose NH2ST, a framework that integrates spatial context and both pathology and gene modalities for gene expression prediction. Our model comprises a query branch and a neighbor branch to process paired target patch and gene data and their neighboring regions, where cross-attention and contrastive learning are employed to capture intrinsic associations and ensure alignments between pathology and gene expression. Extensive experiments on six datasets demonstrate that our model consistently outperforms existing methods, achieving over 20% in PCC metrics. Codes are available at https://github.com/MCPathology/NH2ST

Related papers

• Gene-DML: Dual-Pathway Multi-Level Discrimination for Gene Expression Prediction from Histopathology Images [5.638556074980827]
  Accurately predicting gene expression from histopathology images offers a scalable and non-invasive approach to molecular profiling.<n>Existing methods often underutilize the cross-modal representation alignment between histopathology images and gene expression profiles.<n>We propose Gene-DML, a unified framework that structures latent space through Dual-pathway Multi-Level discrimination.
  arXiv  Detail & Related papers  (2025-07-19T15:45:12Z)
• MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention [52.106879463828044]
  Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease.<n>We present MIRROR, a novel multi-modal representation learning method designed to foster both modality alignment and retention.<n>Extensive evaluations on TCGA cohorts for cancer subtyping and survival analysis highlight MIRROR's superior performance.
  arXiv  Detail & Related papers  (2025-03-01T07:02:30Z)
• Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomics [7.763803040383128]
  We propose a framework named BG-TRIPLEX, which leverages boundary information extracted from pathological images as guiding features to enhance gene expression prediction.<n>Our framework consistently outperforms existing methods in terms of Pearson Correlation Coefficient (PCC)<n>This method highlights the crucial role of boundary features in understanding the complex interactions between WSI and gene expression.
  arXiv  Detail & Related papers  (2024-12-05T11:09:11Z)
• MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Images [6.717786190771243]
  We introduce MERGE (Multi-faceted hiErarchical gRaph for Gene Expressions), which combines a hierarchical graph construction strategy with graph neural networks (GNN) to improve gene expression predictions from whole slide images.<n>By clustering tissue image patches based on both spatial and morphological features, our approach fosters interactions between distant tissue locations during GNN learning.<n>As an additional contribution, we evaluate different data smoothing techniques that are necessary to mitigate artifacts in ST data, often caused by technical imperfections.
  arXiv  Detail & Related papers  (2024-12-03T17:32:05Z)
• Multi-modal Spatial Clustering for Spatial Transcriptomics Utilizing High-resolution Histology Images [1.3124513975412255]
  spatial transcriptomics (ST) enables transcriptome-wide gene expression profiling while preserving spatial context. Current spatial clustering methods fail to fully integrate high-resolution histology image features with gene expression data. We propose a novel contrastive learning-based deep learning approach that integrates gene expression data with histology image features.
  arXiv  Detail & Related papers  (2024-10-31T00:32:24Z)
• Efficient and Scalable Fine-Tune of Language Models for Genome Understanding [49.606093223945734]
  We present textscLingo: textscLanguage prefix ftextscIne-tuning for textscGentextscOmes. Unlike DNA foundation models, textscLingo strategically leverages natural language foundation models' contextual cues. textscLingo further accommodates numerous downstream fine-tune tasks by an adaptive rank sampling method.
  arXiv  Detail & Related papers  (2024-02-12T21:40:45Z)
• 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)
• Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning [78.49090351193269]
  We propose a novel graph-based framework to leverage the inter-relationships among different types of nuclei for WSI analysis. Specifically, we formulate the WSI as a heterogeneous graph with "nucleus-type" attribute to each node and a semantic attribute similarity to each edge. Our framework outperforms the state-of-the-art methods with considerable margins on various tasks.
  arXiv  Detail & Related papers  (2023-07-09T14:43:40Z)
• Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology [57.25098075813054]
  We propose a new framework for gene discovery entitled Un Phenotype Ensembles. It builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations.
  arXiv  Detail & Related papers  (2023-01-07T18:36:44Z)
• Mixed Graph Contrastive Network for Semi-Supervised Node Classification [63.924129159538076]
  We propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN)<n>In our method, we improve the discriminative capability of the latent embeddings by an unperturbed augmentation strategy and a correlation reduction mechanism.<n>By combining the two settings, we extract rich supervision information from both the abundant nodes and the rare yet valuable labeled nodes for discriminative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning [102.9138736545956]
  Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies. In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately.
  arXiv  Detail & Related papers  (2022-04-30T12:57:02Z)

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.