Data Imputation refers to filling in missing values in spatial transcriptomic data, which arises due to limitations in current measurement technologies. This technique is essential for enhancing the resolution and interpretability of gene expression profiles by estimating and replacing incomplete data points. Effective data imputation helps to create a more complete and accurate representation of the spatiotemporal gene expression landscape and cell interactions, thereby improving the overall analysis without relying on additional matched single-cell RNA-seq data or neglecting spatial and expression similarity information.

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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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