Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization

Changxi Chi, Hang Shi, Qi Zhu, Daoqiang Zhang, Wei Shao·June 18, 2024

Summary

The paper introduces ST-GCHB, a multi-view graph contrastive learning framework with HSIC-bottleneck regularization for predicting spatial gene expression from histopathological images. It addresses the lack of spatial dependency in previous models by considering spot relationships and shared information between gene expressions and images. The method enhances accuracy by capturing spatial dependencies and integrating gene expression and histology data. Experiments on the DLPFC dataset demonstrate the model's superiority, showing potential for cost-effective molecular signature prediction from histology. ST-GCHB outperforms existing methods like ST-Net, HE2RNA, and Hist2ST, and an ablation study highlights the importance of spatial information and HSIC-bottleneck regularization. The study contributes to the field of cancer research by offering a novel approach for integrating histology and transcriptomics data for improved gene expression prediction and analysis.

Key findings

2

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of predicting gene expression from histopathological images by leveraging spatial information among different spots in spatial transcriptomics (ST) data . This problem is not entirely new, as previous studies have explored predicting gene expression from histopathological images using various methods . The novelty lies in the approach proposed in the paper, which combines Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization to learn shared representations for gene expression imputation .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that a Multi-view Graph Contrastive Learning framework with HSIC-bottleneck Regularization (ST-GCHB) can effectively predict spatially resolved gene expression from histopathological images by considering spatial dependency among spots and learning shared representations to impute gene expression values . The study focuses on leveraging spatial information from different modalities to enhance gene expression prediction accuracy, addressing the challenge of spatial dependency among spots in spatial transcriptomics data . The experimental results demonstrate the viability and effectiveness of the proposed ST-GCHB model for predicting molecular signatures of tissues from histopathological images .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel method called ST-GCHB (Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization) that aims to predict gene expression values from histopathological images by leveraging spatial information . This method combines Transformer and graph neural network modules to capture spatial relations within the image and neighboring spots . Additionally, the paper introduces a hybrid neural network that utilizes dynamic convolutional and capsule networks to explore the relationship between high-resolution pathology image phenotypes and gene expression data . These approaches highlight the importance of spatial information in improving gene prediction performance .

Furthermore, the paper discusses the incorporation of a HSIC-bottleneck regularization term in the ST-GCHB model to reduce feature redundancy and enhance prediction accuracy . This regularization term is designed to improve the efficiency of feature extraction and reduce noise in the extracted features . The paper evaluates the ST-GCHB model on the dorsolateral prefrontal cortex (DLPFC) dataset, demonstrating its superiority over existing methods in predicting gene expression values .

Moreover, the paper presents an ablation study to investigate the effectiveness of the ST-GCHB method, focusing on the nHSIC-Bottleneck and graph contrastive learning module . The study evaluates different variations of the model by removing or altering specific components to understand their impact on prediction performance . The results suggest that symmetrically considering spatial information from both image and gene modalities leads to promising outcomes in gene expression prediction . This highlights the importance of integrating spatial information effectively to enhance prediction accuracy . The proposed method, ST-GCHB (Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization), offers several key characteristics and advantages compared to previous methods outlined in the paper .

  1. Spatial Dependency Consideration: Unlike previous approaches that treat prediction tasks on each spot of spatial transcriptomics (ST) data independently, ST-GCHB acknowledges the spatial dependency among different spots in ST data. By capturing spatially continuous patterns of gene expression, ST-GCHB leverages spatial information effectively to improve prediction accuracy .

  2. Multi-view Graph Contrastive Learning: ST-GCHB incorporates a Multi-view Graph Contrastive Learning framework to learn shared representations for predicting gene expression values from histopathological images. This approach enables the model to extract meaningful imaging and genomic features by considering their spatial characteristics, leading to enhanced prediction performance .

  3. HSIC-bottleneck Regularization: The inclusion of a HSIC-bottleneck regularization term in the ST-GCHB model helps reduce feature redundancy and enhance prediction accuracy by improving the efficiency of feature extraction. This regularization term plays a crucial role in optimizing the model's performance .

  4. Experimental Superiority: Experimental results on the dorsolateral prefrontal cortex (DLPFC) dataset demonstrate that ST-GCHB outperforms existing methods in predicting gene expression values. The method achieves higher prediction accuracy and effectively identifies spatial gene expression patterns, showcasing its viability and effectiveness in predicting molecular signatures of tissues from histopathological images .

  5. Symmetric Spatial Information Integration: The ablation study conducted on the ST-GCHB method highlights the importance of symmetrically considering spatial information from both image and gene modalities. This approach enables mutual enhancement between the two modalities, leading to promising results in gene expression prediction .

Overall, the ST-GCHB method stands out for its comprehensive consideration of spatial dependency, utilization of multi-view graph contrastive learning, incorporation of HSIC-bottleneck regularization, and experimental superiority in predicting gene expression values from histopathological images. These characteristics collectively contribute to the method's effectiveness and performance in spatially resolved gene expression prediction .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of spatially resolved gene expression prediction from histology images. Noteworthy researchers in this area include Bryan He, Ludvig Bergenstr˚ahle, Linnea Stenbeck, Abubakar Abid, Alma Ander- sson, ˚Ake Borg, Jonas Maaskola, Joakim Lundeberg, James Zou, Benoˆıt Schmauch, Alberto Romagnoni, Elodie Pronier, and many others . These researchers have proposed various methods and models to predict gene expression from histopathological images, such as ST-Net, HE2RNA model, and hist2rna .

The key to the solution mentioned in the paper is the development of a Multi-view Graph Contrastive Learning framework with HSIC-bottleneck Regularization (ST-GCHB). This framework aims at learning shared representations to help impute the gene expression of the queried imaging spots by considering their spatial dependency. The method combines intra-modal graph contrastive learning to learn meaningful imaging and genomic features of spots, incorporates a HSIC-bottleneck regularization term to reduce feature redundancy, and applies cross-modal contrastive learning to align multi-modal data for predicting spatially resolved gene expression data from histopathological images .


How were the experiments in the paper designed?

The experiments in the paper were designed with specific methodologies and settings:

  • The experiments were conducted using a single Nvidia RTX 3090 Ti GPU with the AdamW optimizer to reduce training time costs .
  • The dorsolateral prefrontal cortex (DLPFC) dataset derived from the 10X Visium platform was utilized for testing the method, with details on the number of spots and detected genes provided in Table 1 .
  • The gene expression data derived from spatial transcriptomics (ST) data underwent log normalization and selection of the top 2000 genes with the highest variance using Scanpy .
  • Spatial adjacency information was considered crucial, and the sampling spots were strategically distributed across the STs chip to ensure a uniform spatial distribution. The Graph Contrastive Learning framework DGI was introduced to reveal distribution patterns of gene expression more effectively .
  • The experiments involved predicting gene expression values from histopathological images by extracting features from gene expression of the training set and retrieving similar features for prediction through linear combination using indexing .
  • The correlation of expression prediction was evaluated on selected genes, highly variable genes, and highly expressed genes with the ground truth, showcasing the advantages of the ST-GCHB model over other methods .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the human dorsolateral prefrontal cortex dataset derived from the 10X Visium platform [6] . The code for the method proposed in the study is not explicitly mentioned to be open source in the provided context. Therefore, it is advisable to refer to the original source or contact the authors directly for information regarding the availability of the code .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed to be verified. The paper introduces a Multi-view Graph Contrastive Learning framework with HSIC-bottleneck Regularization (ST-GCHB) to predict gene expression from histopathological images by considering spatial dependencies among different spots . The experiments conducted on the dorsolateral prefrontal cortex (DLPFC) dataset demonstrate a significant improvement compared to existing approaches, indicating the viability and effectiveness of the ST-GCHB model for predicting molecular signatures of tissues from histopathological images .

Furthermore, the paper evaluates the correlation of expression prediction on selected genes and compares the predicted expression with the ground truth, showing that the ST-GCHB model and BLEEP exhibit significant advantages over other methods . These approaches address the curse of dimensionality issue by aligning and exploring features from different modalities in the latent space, enabling effective prediction of a limited number of gene types .

Moreover, an ablation study is conducted to investigate the effectiveness of the ST-GCHB model, focusing on the nHSIC-Bottleneck and graph contrastive learning module. The results show that unilaterally considering spatial information from one modality may dilute the impact, while symmetrically considering spatial information from both modalities enhances the prediction performance, supporting the hypothesis that leveraging spatial information from multiple modalities improves prediction accuracy .


What are the contributions of this paper?

The paper titled "Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization" makes several key contributions :

  1. Proposed Framework: The paper introduces a Multi-view Graph Contrastive Learning framework with HSIC-bottleneck Regularization (ST-GCHB) to predict gene expression from histopathological images by considering spatial dependencies among spots .

  2. Shared Representation Learning: The framework aims at learning shared representations to impute gene expression of queried imaging spots by incorporating spatial information and enhancing the efficiency of the model through HSIC-bottleneck regularization .

  3. Experimental Results: The study conducted experiments on the dorsolateral prefrontal cortex (DLPFC) dataset and observed a significant improvement compared to existing approaches, demonstrating the viability and effectiveness of the proposed ST-GCHB method for predicting molecular signatures of tissues from histopathological images .


What work can be continued in depth?

Further research in the field of spatially resolved gene expression prediction from histology can be expanded in several directions:

  • Exploring Spatial Dependency: Future studies can delve deeper into understanding and leveraging the spatial dependency among different spots in spatial transcriptomic (ST) data to enhance gene expression prediction accuracy .
  • Enhancing Model Efficiency: Researchers can focus on developing more efficient deep learning architectures that can effectively handle the high dimensionality of single-cell transcriptomic data and spatial information to improve prediction outcomes .
  • Integrating Multi-modal Data: There is potential for further research in integrating multi-modal data, such as gene expression profiles and histopathological images, to predict gene expression values more accurately by aligning different data modalities effectively .
  • Utilizing Graph Contrastive Learning: The utilization of Graph Contrastive Learning frameworks, like DGI, can be explored to reveal distribution patterns of gene expression more effectively by considering the spatial relationships among sampling spots on the STs chip .
  • Addressing Spatial Expression Patterns: Researchers can focus on deciphering spatially continuous patterns of gene expression among different spots in ST data to improve the performance of gene prediction models .
  • Improving Spatial Transcriptomics Technology: Continuous advancements in spatial transcriptomics technology can further transform genetic research by enabling the measurement of gene expression at spatial resolution, facilitating more comprehensive studies in this domain .

Tables

1

Introduction
Background
Evolution of gene expression prediction from histopathology
Limitations of previous models in capturing spatial dependencies
Objective
To develop a novel framework for spatial gene expression prediction
Integrate spot relationships and shared information between gene expressions and histology
Improve accuracy and molecular signature prediction from histology
Method
Data Collection
Histopathological image acquisition
Gene expression spot data collection
Data Preprocessing
Spot annotation and extraction
Image feature extraction (e.g., texture, intensity)
Graph construction (spot relationships and gene expression similarities)
Spatial Graph Contrastive Learning
HSIC-Bottleneck Regularization
Introduce HSIC (Hilbert-Schmidt Independence Criterion) to preserve dependencies
Regularization to enhance integration of gene expression and histology views
Multi-View Learning
Joint embedding of gene expression and histology data
Contrastive loss for learning discriminative representations
Model Architecture
ST-GCHB architecture description
Spot-level and global context encoding
Experimentation
DLPFC Dataset
Dataset overview and preprocessing
Evaluation metrics (accuracy, AUC, etc.)
Comparison with Existing Methods
ST-Net, HE2RNA, and Hist2ST performance comparison
Statistical significance testing
Ablation Study
Impact of spatial information and HSIC-bottleneck regularization
Analysis of model variants
Results and Discussion
ST-GCHB performance improvements
Clinical implications and potential applications
Limitations and future directions
Conclusion
Summary of contributions to cancer research
Implications for integrating histology and transcriptomics data
Future research possibilities in the field
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
How does ST-GCHB address the limitations of previous models in spatial gene expression prediction?
What dataset is used to evaluate the performance of ST-GCHB, and what is its significance?
How does ST-GCHB compare to other methods like ST-Net, HE2RNA, and Hist2ST in terms of accuracy?
What is the primary focus of the paper ST-GCHB?

Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization

Changxi Chi, Hang Shi, Qi Zhu, Daoqiang Zhang, Wei Shao·June 18, 2024

Summary

The paper introduces ST-GCHB, a multi-view graph contrastive learning framework with HSIC-bottleneck regularization for predicting spatial gene expression from histopathological images. It addresses the lack of spatial dependency in previous models by considering spot relationships and shared information between gene expressions and images. The method enhances accuracy by capturing spatial dependencies and integrating gene expression and histology data. Experiments on the DLPFC dataset demonstrate the model's superiority, showing potential for cost-effective molecular signature prediction from histology. ST-GCHB outperforms existing methods like ST-Net, HE2RNA, and Hist2ST, and an ablation study highlights the importance of spatial information and HSIC-bottleneck regularization. The study contributes to the field of cancer research by offering a novel approach for integrating histology and transcriptomics data for improved gene expression prediction and analysis.
Mind map
Statistical significance testing
ST-Net, HE2RNA, and Hist2ST performance comparison
Evaluation metrics (accuracy, AUC, etc.)
Dataset overview and preprocessing
Contrastive loss for learning discriminative representations
Joint embedding of gene expression and histology data
Regularization to enhance integration of gene expression and histology views
Introduce HSIC (Hilbert-Schmidt Independence Criterion) to preserve dependencies
Analysis of model variants
Impact of spatial information and HSIC-bottleneck regularization
Comparison with Existing Methods
DLPFC Dataset
Spot-level and global context encoding
ST-GCHB architecture description
Multi-View Learning
HSIC-Bottleneck Regularization
Graph construction (spot relationships and gene expression similarities)
Image feature extraction (e.g., texture, intensity)
Spot annotation and extraction
Gene expression spot data collection
Histopathological image acquisition
Improve accuracy and molecular signature prediction from histology
Integrate spot relationships and shared information between gene expressions and histology
To develop a novel framework for spatial gene expression prediction
Limitations of previous models in capturing spatial dependencies
Evolution of gene expression prediction from histopathology
Future research possibilities in the field
Implications for integrating histology and transcriptomics data
Summary of contributions to cancer research
Limitations and future directions
Clinical implications and potential applications
ST-GCHB performance improvements
Ablation Study
Experimentation
Model Architecture
Spatial Graph Contrastive Learning
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Evolution of gene expression prediction from histopathology
Limitations of previous models in capturing spatial dependencies
Objective
To develop a novel framework for spatial gene expression prediction
Integrate spot relationships and shared information between gene expressions and histology
Improve accuracy and molecular signature prediction from histology
Method
Data Collection
Histopathological image acquisition
Gene expression spot data collection
Data Preprocessing
Spot annotation and extraction
Image feature extraction (e.g., texture, intensity)
Graph construction (spot relationships and gene expression similarities)
Spatial Graph Contrastive Learning
HSIC-Bottleneck Regularization
Introduce HSIC (Hilbert-Schmidt Independence Criterion) to preserve dependencies
Regularization to enhance integration of gene expression and histology views
Multi-View Learning
Joint embedding of gene expression and histology data
Contrastive loss for learning discriminative representations
Model Architecture
ST-GCHB architecture description
Spot-level and global context encoding
Experimentation
DLPFC Dataset
Dataset overview and preprocessing
Evaluation metrics (accuracy, AUC, etc.)
Comparison with Existing Methods
ST-Net, HE2RNA, and Hist2ST performance comparison
Statistical significance testing
Ablation Study
Impact of spatial information and HSIC-bottleneck regularization
Analysis of model variants
Results and Discussion
ST-GCHB performance improvements
Clinical implications and potential applications
Limitations and future directions
Conclusion
Summary of contributions to cancer research
Implications for integrating histology and transcriptomics data
Future research possibilities in the field
Key findings
2

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of predicting gene expression from histopathological images by leveraging spatial information among different spots in spatial transcriptomics (ST) data . This problem is not entirely new, as previous studies have explored predicting gene expression from histopathological images using various methods . The novelty lies in the approach proposed in the paper, which combines Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization to learn shared representations for gene expression imputation .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that a Multi-view Graph Contrastive Learning framework with HSIC-bottleneck Regularization (ST-GCHB) can effectively predict spatially resolved gene expression from histopathological images by considering spatial dependency among spots and learning shared representations to impute gene expression values . The study focuses on leveraging spatial information from different modalities to enhance gene expression prediction accuracy, addressing the challenge of spatial dependency among spots in spatial transcriptomics data . The experimental results demonstrate the viability and effectiveness of the proposed ST-GCHB model for predicting molecular signatures of tissues from histopathological images .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper proposes a novel method called ST-GCHB (Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization) that aims to predict gene expression values from histopathological images by leveraging spatial information . This method combines Transformer and graph neural network modules to capture spatial relations within the image and neighboring spots . Additionally, the paper introduces a hybrid neural network that utilizes dynamic convolutional and capsule networks to explore the relationship between high-resolution pathology image phenotypes and gene expression data . These approaches highlight the importance of spatial information in improving gene prediction performance .

Furthermore, the paper discusses the incorporation of a HSIC-bottleneck regularization term in the ST-GCHB model to reduce feature redundancy and enhance prediction accuracy . This regularization term is designed to improve the efficiency of feature extraction and reduce noise in the extracted features . The paper evaluates the ST-GCHB model on the dorsolateral prefrontal cortex (DLPFC) dataset, demonstrating its superiority over existing methods in predicting gene expression values .

Moreover, the paper presents an ablation study to investigate the effectiveness of the ST-GCHB method, focusing on the nHSIC-Bottleneck and graph contrastive learning module . The study evaluates different variations of the model by removing or altering specific components to understand their impact on prediction performance . The results suggest that symmetrically considering spatial information from both image and gene modalities leads to promising outcomes in gene expression prediction . This highlights the importance of integrating spatial information effectively to enhance prediction accuracy . The proposed method, ST-GCHB (Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization), offers several key characteristics and advantages compared to previous methods outlined in the paper .

  1. Spatial Dependency Consideration: Unlike previous approaches that treat prediction tasks on each spot of spatial transcriptomics (ST) data independently, ST-GCHB acknowledges the spatial dependency among different spots in ST data. By capturing spatially continuous patterns of gene expression, ST-GCHB leverages spatial information effectively to improve prediction accuracy .

  2. Multi-view Graph Contrastive Learning: ST-GCHB incorporates a Multi-view Graph Contrastive Learning framework to learn shared representations for predicting gene expression values from histopathological images. This approach enables the model to extract meaningful imaging and genomic features by considering their spatial characteristics, leading to enhanced prediction performance .

  3. HSIC-bottleneck Regularization: The inclusion of a HSIC-bottleneck regularization term in the ST-GCHB model helps reduce feature redundancy and enhance prediction accuracy by improving the efficiency of feature extraction. This regularization term plays a crucial role in optimizing the model's performance .

  4. Experimental Superiority: Experimental results on the dorsolateral prefrontal cortex (DLPFC) dataset demonstrate that ST-GCHB outperforms existing methods in predicting gene expression values. The method achieves higher prediction accuracy and effectively identifies spatial gene expression patterns, showcasing its viability and effectiveness in predicting molecular signatures of tissues from histopathological images .

  5. Symmetric Spatial Information Integration: The ablation study conducted on the ST-GCHB method highlights the importance of symmetrically considering spatial information from both image and gene modalities. This approach enables mutual enhancement between the two modalities, leading to promising results in gene expression prediction .

Overall, the ST-GCHB method stands out for its comprehensive consideration of spatial dependency, utilization of multi-view graph contrastive learning, incorporation of HSIC-bottleneck regularization, and experimental superiority in predicting gene expression values from histopathological images. These characteristics collectively contribute to the method's effectiveness and performance in spatially resolved gene expression prediction .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of spatially resolved gene expression prediction from histology images. Noteworthy researchers in this area include Bryan He, Ludvig Bergenstr˚ahle, Linnea Stenbeck, Abubakar Abid, Alma Ander- sson, ˚Ake Borg, Jonas Maaskola, Joakim Lundeberg, James Zou, Benoˆıt Schmauch, Alberto Romagnoni, Elodie Pronier, and many others . These researchers have proposed various methods and models to predict gene expression from histopathological images, such as ST-Net, HE2RNA model, and hist2rna .

The key to the solution mentioned in the paper is the development of a Multi-view Graph Contrastive Learning framework with HSIC-bottleneck Regularization (ST-GCHB). This framework aims at learning shared representations to help impute the gene expression of the queried imaging spots by considering their spatial dependency. The method combines intra-modal graph contrastive learning to learn meaningful imaging and genomic features of spots, incorporates a HSIC-bottleneck regularization term to reduce feature redundancy, and applies cross-modal contrastive learning to align multi-modal data for predicting spatially resolved gene expression data from histopathological images .


How were the experiments in the paper designed?

The experiments in the paper were designed with specific methodologies and settings:

  • The experiments were conducted using a single Nvidia RTX 3090 Ti GPU with the AdamW optimizer to reduce training time costs .
  • The dorsolateral prefrontal cortex (DLPFC) dataset derived from the 10X Visium platform was utilized for testing the method, with details on the number of spots and detected genes provided in Table 1 .
  • The gene expression data derived from spatial transcriptomics (ST) data underwent log normalization and selection of the top 2000 genes with the highest variance using Scanpy .
  • Spatial adjacency information was considered crucial, and the sampling spots were strategically distributed across the STs chip to ensure a uniform spatial distribution. The Graph Contrastive Learning framework DGI was introduced to reveal distribution patterns of gene expression more effectively .
  • The experiments involved predicting gene expression values from histopathological images by extracting features from gene expression of the training set and retrieving similar features for prediction through linear combination using indexing .
  • The correlation of expression prediction was evaluated on selected genes, highly variable genes, and highly expressed genes with the ground truth, showcasing the advantages of the ST-GCHB model over other methods .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the human dorsolateral prefrontal cortex dataset derived from the 10X Visium platform [6] . The code for the method proposed in the study is not explicitly mentioned to be open source in the provided context. Therefore, it is advisable to refer to the original source or contact the authors directly for information regarding the availability of the code .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed to be verified. The paper introduces a Multi-view Graph Contrastive Learning framework with HSIC-bottleneck Regularization (ST-GCHB) to predict gene expression from histopathological images by considering spatial dependencies among different spots . The experiments conducted on the dorsolateral prefrontal cortex (DLPFC) dataset demonstrate a significant improvement compared to existing approaches, indicating the viability and effectiveness of the ST-GCHB model for predicting molecular signatures of tissues from histopathological images .

Furthermore, the paper evaluates the correlation of expression prediction on selected genes and compares the predicted expression with the ground truth, showing that the ST-GCHB model and BLEEP exhibit significant advantages over other methods . These approaches address the curse of dimensionality issue by aligning and exploring features from different modalities in the latent space, enabling effective prediction of a limited number of gene types .

Moreover, an ablation study is conducted to investigate the effectiveness of the ST-GCHB model, focusing on the nHSIC-Bottleneck and graph contrastive learning module. The results show that unilaterally considering spatial information from one modality may dilute the impact, while symmetrically considering spatial information from both modalities enhances the prediction performance, supporting the hypothesis that leveraging spatial information from multiple modalities improves prediction accuracy .


What are the contributions of this paper?

The paper titled "Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization" makes several key contributions :

  1. Proposed Framework: The paper introduces a Multi-view Graph Contrastive Learning framework with HSIC-bottleneck Regularization (ST-GCHB) to predict gene expression from histopathological images by considering spatial dependencies among spots .

  2. Shared Representation Learning: The framework aims at learning shared representations to impute gene expression of queried imaging spots by incorporating spatial information and enhancing the efficiency of the model through HSIC-bottleneck regularization .

  3. Experimental Results: The study conducted experiments on the dorsolateral prefrontal cortex (DLPFC) dataset and observed a significant improvement compared to existing approaches, demonstrating the viability and effectiveness of the proposed ST-GCHB method for predicting molecular signatures of tissues from histopathological images .


What work can be continued in depth?

Further research in the field of spatially resolved gene expression prediction from histology can be expanded in several directions:

  • Exploring Spatial Dependency: Future studies can delve deeper into understanding and leveraging the spatial dependency among different spots in spatial transcriptomic (ST) data to enhance gene expression prediction accuracy .
  • Enhancing Model Efficiency: Researchers can focus on developing more efficient deep learning architectures that can effectively handle the high dimensionality of single-cell transcriptomic data and spatial information to improve prediction outcomes .
  • Integrating Multi-modal Data: There is potential for further research in integrating multi-modal data, such as gene expression profiles and histopathological images, to predict gene expression values more accurately by aligning different data modalities effectively .
  • Utilizing Graph Contrastive Learning: The utilization of Graph Contrastive Learning frameworks, like DGI, can be explored to reveal distribution patterns of gene expression more effectively by considering the spatial relationships among sampling spots on the STs chip .
  • Addressing Spatial Expression Patterns: Researchers can focus on deciphering spatially continuous patterns of gene expression among different spots in ST data to improve the performance of gene prediction models .
  • Improving Spatial Transcriptomics Technology: Continuous advancements in spatial transcriptomics technology can further transform genetic research by enabling the measurement of gene expression at spatial resolution, facilitating more comprehensive studies in this domain .
Tables
1
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