The proposed cross-modal diffusion modeling framework can be extended to incorporate additional modalities, such as spatial proteomics or imaging mass spectrometry, by adapting the existing architecture to accommodate the unique characteristics of these data types.
Integration of Spatial Proteomics: Spatial proteomics data provides information on the spatial distribution of proteins within tissues. To incorporate this modality, the model can be modified to handle protein expression levels in addition to gene expression. This would involve adjusting the input features, conditioning mechanisms, and feature extraction processes to capture the spatial distribution of proteins alongside genes.
Imaging Mass Spectrometry: Imaging mass spectrometry (IMS) generates spatially resolved molecular information, allowing for the visualization of various biomolecules. By integrating IMS data, the model can learn to predict the spatial distribution of metabolites or lipids in conjunction with gene expression. This would require adapting the network architecture to process the unique data format of IMS and extract relevant features for multimodal fusion.
Multimodal Integration: To enhance the spatial resolution and multimodal integration of biological data, the framework can incorporate attention mechanisms or graph neural networks to capture complex relationships between different modalities. Attention mechanisms can help the model focus on relevant regions in each modality, while graph neural networks can model interactions between genes, proteins, and metabolites in a spatial context.
By extending the cross-modal diffusion modeling framework to include spatial proteomics and imaging mass spectrometry, researchers can gain a more comprehensive understanding of the spatial organization and interactions of multiple biological molecules within tissues.