Cross-modal Diffusion Modelling for Super-resolved Spatial Transcriptomics

• URL: http://arxiv.org/abs/2404.12973v2
• Date: Mon, 27 May 2024 13:43:30 GMT
• Title: Cross-modal Diffusion Modelling for Super-resolved Spatial Transcriptomics
• Authors: Xiaofei Wang, Xingxu Huang, Stephen J. Price, Chao Li,
• Abstract summary: spatial transcriptomics allows to characterize spatial gene expression within tissue for discovery research. Super-resolution approaches promise to enhance ST maps by integrating histology images with gene expressions of profiled tissue spots. This paper proposes a cross-modal conditional diffusion model for super-resolving ST maps with the guidance of histology images.
• Score: 5.020980014307814
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent advancement of spatial transcriptomics (ST) allows to characterize spatial gene expression within tissue for discovery research. However, current ST platforms suffer from low resolution, hindering in-depth understanding of spatial gene expression. Super-resolution approaches promise to enhance ST maps by integrating histology images with gene expressions of profiled tissue spots. However, current super-resolution methods are limited by restoration uncertainty and mode collapse. Although diffusion models have shown promise in capturing complex interactions between multi-modal conditions, it remains a challenge to integrate histology images and gene expression for super-resolved ST maps. This paper proposes a cross-modal conditional diffusion model for super-resolving ST maps with the guidance of histology images. Specifically, we design a multi-modal disentangling network with cross-modal adaptive modulation to utilize complementary information from histology images and spatial gene expression. Moreover, we propose a dynamic cross-attention modelling strategy to extract hierarchical cell-to-tissue information from histology images. Lastly, we propose a co-expression-based gene-correlation graph network to model the co-expression relationship of multiple genes. Experiments show that our method outperforms other state-of-the-art methods in ST super-resolution on three public datasets.

Related papers

• 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)
• High-Resolution Spatial Transcriptomics from Histology Images using HisToSGE [1.3124513975412255]
  HisToSGE generates high-resolution gene expression profiles from histological images. HisToSGE excels in generating high-resolution gene expression profiles and performing downstream tasks.
  arXiv  Detail & Related papers  (2024-07-30T03:29:57Z)
• SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq [9.624390863643109]
  SpaDiT is a deep learning method that integrates scRNA-seq and ST data for the prediction of undetected genes. We have demonstrated the effectiveness of SpaDiT through extensive experiments on both seq-based and image-based ST data.
  arXiv  Detail & Related papers  (2024-07-18T05:40:50Z)
• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)
• Multimodal contrastive learning for spatial gene expression prediction using histology images [13.47034080678041]
  We propose textbfmclSTExp, a multimodal contrastive learning with Transformer and Densenet-121 encoder for Spatial Transcriptomics Expression prediction. textbfmclSTExp has superior performance in predicting spatial gene expression. It has shown promise in interpreting cancer-specific overexpressed genes, elucidating immune-related genes, and identifying specialized spatial domains annotated by pathologists.
  arXiv  Detail & Related papers  (2024-07-11T06:33:38Z)
• GTP-4o: Modality-prompted Heterogeneous Graph Learning for Omni-modal Biomedical Representation [68.63955715643974]
  Modality-prompted Heterogeneous Graph for Omnimodal Learning (GTP-4o) We propose an innovative Modality-prompted Heterogeneous Graph for Omnimodal Learning (GTP-4o)
  arXiv  Detail & Related papers  (2024-07-08T01:06:13Z)
• Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization [18.554968935341236]
  We propose a Multi-view Graph Contrastive Learning framework with HSIC-bottleneck Regularization(ST-GCHB) to help impute the gene expression of the queried imagingspots by considering their spatial dependency.
  arXiv  Detail & Related papers  (2024-06-18T03:07:25Z)
• EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models [52.3015009878545]
  We develop an image segmentor capable of generating fine-grained segmentation maps without any additional training. Our framework identifies semantic correspondences between image pixels and spatial locations of low-dimensional feature maps. In extensive experiments, the produced segmentation maps are demonstrated to be well delineated and capture detailed parts of the images.
  arXiv  Detail & Related papers  (2024-01-22T07:34:06Z)
• Learned representation-guided diffusion models for large-image generation [58.192263311786824]
  We introduce a novel approach that trains diffusion models conditioned on embeddings from self-supervised learning (SSL) Our diffusion models successfully project these features back to high-quality histopathology and remote sensing images. Augmenting real data by generating variations of real images improves downstream accuracy for patch-level and larger, image-scale classification tasks.
  arXiv  Detail & Related papers  (2023-12-12T14:45:45Z)
• DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion [144.9653045465908]
  We propose a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM) Our approach yields promising fusion results in infrared-visible image fusion and medical image fusion.
  arXiv  Detail & Related papers  (2023-03-13T04:06:42Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.