Accurate Spatial Gene Expression Prediction by integrating Multi-resolution features

2024/03/12 by Youngmin Chung, Chung, Youngmin, Ji Hun Ha +5 · 15 citations
Biochemistry, Genetics and Molecular Biology · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Gene expression and cancer classification #Machine Learning in Bioinformatics

paper · pdf · doi:10.48550/arxiv.2403.07592

openalex publication_date 2024/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Recent advancements in Spatial Transcriptomics (ST) technology have facilitated detailed gene expression analysis within tissue contexts. However, the high costs and methodological limitations of ST necessitate a more robust predictive model. In response, this paper introduces TRIPLEX, a novel deep learning framework designed to predict spatial gene expression from Whole Slide Images (WSIs). TRIPLEX uniquely harnesses multi-resolution features, capturing cellular morphology at individual spots, the local context around these spots, and the global tissue organization. By integrating these features through an effective fusion strategy, TRIPLEX achieves accurate gene expression prediction. Our comprehensive benchmark study, conducted on three public ST datasets and supplemented with Visium data from 10X Genomics, demonstrates that TRIPLEX outperforms current state-of-the-art models in Mean Squared Error (MSE), Mean Absolute Error (MAE), and Pearson Correlation Coefficient (PCC). The model's predictions align closely with ground truth gene expression profiles and tumor annotations, underscoring TRIPLEX's potential in advancing cancer diagnosis and treatment.

Cited by

• HistoGPA: A Context-Conditioned Gene-Prior Attention Framework for Histology-Based Spatial Gene Expression Prediction
• SCR2-ST: Combine Single Cell with Spatial Transcriptomics for Efficient Active Sampling via Reinforcement Learning
• Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics
• HyperST: Hierarchical Hyperbolic Learning for Spatial Transcriptomics Prediction
• Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selection
• Dual-Path Knowledge-Augmented Contrastive Alignment Network for Spatially Resolved Transcriptomics
• SIGMMA: Hierarchical Graph-Based Multi-Scale Multi-modal Contrastive Alignment of Histopathology Image and Spatial Transcriptome
• 3D-Guided Scalable Flow Matching for Generating Volumetric Tissue Spatial Transcriptomics from Serial Histology
• HiFusion: Hierarchical Intra-Spot Alignment and Regional Context Fusion for Spatial Gene Expression Prediction from Histopathology
• SENCA-st: Integrating Spatial Transcriptomics and Histopathology with Cross Attention Shared Encoder for Region Identification in Cancer Pathology
• MMAP: A Multi-Magnification and Prototype-Aware Architecture for Predicting Spatial Gene Expression
• GenAR: Next-Scale Autoregressive Generation for Spatial Gene Expression Prediction
• Learning from Gene Names, Expression Values and Images: Contrastive Masked Text-Image Pretraining for Spatial Transcriptomics Representation Learning
• Img2ST-Net: Efficient High-Resolution Spatial Omics Prediction from Whole Slide Histology Images via Fully Convolutional Image-to-Image Learning
• MV_Hybrid: Improving Spatial Transcriptomics Prediction with Hybrid State Space-Vision Transformer Backbone in Pathology Vision Foundation Models

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• Spatial Transcriptomics Expression Prediction from Histopathology Based on Cross-Modal Mask Reconstruction and Contrastive Learning
• Completing Spatial Transcriptomics Data for Gene Expression Prediction Benchmarking