Reliable imputation of spatial transcriptomes with uncertainty estimation and spatial regularization

Abstract

Imputation of missing features in spatial transcriptomics is urgently needed due to technological limitations. However, most existing computational methods suffer from moderate accuracy and cannot estimate the reliability of the imputation. To fill this research gap, we introduce a computational model, TransImpute, that imputes the missing feature modality in spatial transcriptomics by mapping it from single-cell reference data. We derive a set of attributes that can accurately predict imputation uncertainty, enabling us to select reliably imputed genes. In addition, we introduce a spatial autocorrelation metric as a regularization to avoid overestimating spatial patterns. Multiple datasets from various platforms demonstrate that our approach significantly improves the reliability of downstream analyses in detecting spatial variable genes and interacting ligand-receptor pairs. Therefore, TransImpute offers a reliable approach to spatial analysis of missing features for both matched and unseen modalities, such as nascent RNAs.