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Abstract Spatial transcriptomics technologies enable high-throughput quantification of gene expression at specific locations across tissue sections, facilitating insights into the spatial organization of biological processes. However, high costs associated with these technologies have motivated the development of deep learning methods to predict spatial gene expression from inexpensive hematoxylin and eosin-stained histology images. While most efforts have focused on modifying model architectures to boost predictive performance, the influence of training data quality remains largely unexplored. Here, we investigate how variation in molecular and image data quality stemming from differences in imaging (Xenium) versus sequencing (Visium) spatial transcriptomics technologies impact deep learning-based gene expression prediction from histology images. To delineate the aspects of data quality that impact predictive performance, we conductedin silicoablation experiments, which showed that increased sparsity and noise in molecular data degraded predictive performance, whilein silicorescue experiments via imputation provided only limited improvements that failed to generalize beyond the test set. Likewise, reduced image resolution can degrade predictive performance and further impacts model interpretability. Overall, our results underscore how improving data quality offers an orthogonal strategy to tuning model architecture in enhancing predictive modeling using spatial transcriptomics and emphasize the need for careful consideration of technological limitations that directly impact data quality when developing predictive methodologies.
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iIMPACT: integrating image and molecular profiles for spatial transcriptomics analysis
Abstract Current clustering analysis of spatial transcriptomics data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, we have developed a multi-stage statistical method called iIMPACT. It identifies and defines histology-based spatial domains based on AI-reconstructed histology images and spatial context of gene expression measurements, and detects domain-specific differentially expressed genes. Through multiple case studies, we demonstrate iIMPACT outperforms existing methods in accuracy and interpretability and provides insights into the cellular spatial organization and landscape of functional genes within spatial transcriptomics data.
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- Award ID(s):
- 2210912
- PAR ID:
- 10512736
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Genome Biology
- Volume:
- 25
- Issue:
- 1
- ISSN:
- 1474-760X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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