Spatial Transcriptomics is a cutting-edge technology that allows for the measurement of gene expression in specific spatial contexts. It provides detailed insights into how genes are expressed across different locations within tissues at cellular or even subcellular resolution. This technology captures the complex spatial and temporal dynamics of gene expression and the interactions between cells within their native environments. Despite its potential, current spatial transcriptomic techniques often produce incomplete data with many missing gene expression values, necessitating the use of gene imputation methods to enhance data quality and interpretability.

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Predicting Spatially Resolved Gene Expression via Tissue Morphology using Adaptive Spatial GNNs (ECCB)

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Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images provides a scalable alternative approach to decoding tissue complexity.

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Spatially Informed Gene Signatures for Response to Immunotherapy in Melanoma

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We aim to improve the prediction of response or resistance to immunotherapies in patients with melanoma. This goal is based on the hypothesis that current gene signatures predicting immunotherapy outcomes show only modest accuracy due to the lack of spatial information about cellular functions and molecular processes within tumors and their microenvironment.

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Predicting Spatially Resolved Gene Expression via Tissue Morphology using Adaptive Spatial GNNs

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Motivation Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images, provides a scalable alternative approach to decoding tissue complexity

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Impeller: A Path-based Heterogeneous Graph Learning Method for Spatial Transcriptomic Data Imputation

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Recent advances in spatial transcriptomics allow spatially resolved gene expression measurements with cellular or even sub-cellular resolution, directly characterizing the complex spatiotemporal gene expression landscape and cell-to-cell interactions in their native microenvironments. Due to technology limitations, most spatial transcriptomic technologies still yield incomplete expression measurements with excessive missing values. Therefore, gene imputation is critical to filling in missing data, enhancing resolution, and improving overall interpretability. However, existing methods either require additional matched single-cell RNA-seq data, which is rarely available, or ignore spatial proximity or expression similarity information

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