GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics
On the map: Mixture-of-experts → Medical and biomedical MoE
Abstract (arXiv)
Histology-based single-cell spatial transcriptomics (ST) estimation aims to predict gene expression for individual cells from histopathological images and cell locations, reducing the need for costly single-cell ST measurements. Unlike existing histology-to-ST methods that mainly predict spot-level profiles for local regions containing multiple cells, this task requires modeling cell-to-cell expression variability, which is strongly structured by cell type. We propose Genomics-Guided Cell-Type-Specific Mixture-of-Experts (GC-MoE), which estimates cell-type probabilities with a routing network and softly combines cell-type-specific experts for gene expression prediction. To further encode cell-type-dependent gene programs, we introduce the Cell-Type-Specific Co-Expression-Aware Predictor (CAP), together with a lightweight Cell-to-Cell Interaction Attention (C2CA) module for neighboring-cell context. Experiments and ablations on public single-cell ST datasets show consistent improvements over existing single-cell and adapted spot-level baselines.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts. Builds on 12 of its 33 references that are in the map. First posted on arXiv 1 Jun 2026.
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12 of 33 references.
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Same direction, newest first
- 2026SegMoTE: Token-Level Mixture of Experts for Medical Image Segmentation22 Feb
- 2025Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentation13 Apr
Other references · 10
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2024HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysiscited by 104
- 2023Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learningcited by 77
- 2024Accurate Spatial Gene Expression Prediction by integrating Multi-resolution featurescited by 52
- 2022Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Predictioncited by 39
- 2025CL-MoE: Enhancing Multimodal Large Language Model with Dual Momentum Mixture-of-Experts for Continual Visual Question Answeringcited by 36
- 2024MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Imagescited by 17
- 2026Leveraging Spatial Transcriptomics as Alternative to Manual Annotations for Deep Learning-Based Nuclei Analysiscited by 1
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- 2026Adapting a Pre-trained Single-Cell Foundation Model to Spatial Gene Expression Generation from Histology Images
- 2025Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching
- 2026MoLF: Mixture-of-Latent-Flow for Pan-Cancer Spatial Gene Expression Prediction from Histology
- 2025GeneFlow: Translation of Single-cell Gene Expression to Histopathological Images via Rectified Flow
- 2026Correlation-Guided Flow Matching with Annealed Masking for Spatial Transcriptomics Generation
- 2026Program-space Diffusion for Morphology-to-Transcriptomics Prediction
- 2025SCR2-ST: Combine Single Cell with Spatial Transcriptomics for Efficient Active Sampling via Reinforcement Learning
- 2024Single-Cell Omics Arena: A Benchmark Study for Large Language Models on Cell Type Annotation Using Single-Cell Data