MOTIVATION: 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. RESULTS: To address these issues, we introduce Impeller, a path-based heterogeneous graph learning method for spatial transcriptomic data imputation. Impeller has two unique characteristics distinct from existing approaches. First, it builds a heterogeneous graph with two types of edges representing spatial proximity and expression similarity. Therefore, Impeller can simultaneously model smooth gene expression changes across spatial dimensions and capture similar gene expression signatures of faraway cells from the same type. Moreover, Impeller incorporates both short- and long-range cell-to-cell interactions (e.g. via paracrine and endocrine) by stacking multiple GNN layers. We use a learnable path operator in Impeller to avoid the over-smoothing issue of the traditional Laplacian matrices. Extensive experiments on diverse datasets from three popular platforms and two species demonstrate the superiority of Impeller over various state-of-the-art imputation methods. AVAILABILITY AND IMPLEMENTATION: The code and preprocessed data used in this study are available at https://github.com/aicb-ZhangLabs/Impeller and https://zenodo.org/records/11212604.

Many people practiced COVID-19-related safety measures in the first year of the pandemic, but Republicans were less likely to engage in behaviors such as wearing masks or face coverings than Democrats, suggesting radical disparities in health practices split along political fault lines. We developed an "intervention tournament" which aimed to identify the framings that would promote mask wearing among a representative sample of Republicans and Democrats in the U.S. from Oct 14, 2020, to Jan 14, 2021 (N = 4931). Seven different conditions reflecting different moral values and factors specific to COVID-19-including protection from harm (self), protection from harm (community), patriotic duty, purity, reviving the economy, threat, and scientific evidence-were implemented to identify which framings would "win" in terms of promoting mask wearing compared to a baseline condition. We found that Republicans had significantly more negative attitudes toward masks, lower intentions to wear them, and were less likely to sign or share pledges on social media than Democrats, which was partially mediated by Republicans, compared to Democrats, perceiving that the threat of COVID-19 was lower. None of our framing conditions significantly affected Republicans' or Democrats' attitudes, intentions, or behaviors compared to the baseline condition, illustrating the difficulty in overcoming the strength of political polarization during COVID-19.