ArticlePatterns (New York, N.Y.)2024
Reliable imputation of spatial transcriptomes with uncertainty estimation and spatial regularization.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model.Genome research · 2026Article
- FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.Nature communications · 2026Article
- Hist2Cell: Deciphering fine-grained cellular architectures from histology images.Cell genomics · 2026Article
- VISTA uncovers missing gene expression and spatial-induced information for spatial transcriptomic data analysis.Communications biology · 2026Article
- SpaGene: A Deep Adversarial Framework for Spatial Gene Imputation.Computational and structural biotechnology journal · 2026Article
- VISTA Uncovers Missing Gene Expression and Spatial-induced Information for Spatial Transcriptomic Data Analysis.Research square · 2025Article
- spRefine Denoises and Imputes Spatial Transcriptomics with a Reference-Free Framework Powered by Genomic Language Model.bioRxiv : the preprint server for biology · 2025Article
- Imputing spatial transcriptomics through gene network constructed from protein language model.Communications biology · 2024Article
- CellContrast: Reconstructing spatial relationships in single-cell RNA sequencing data via deep contrastive learning.Patterns (New York, N.Y.) · 2024Article
- Reliable imputation of spatial transcriptomes with uncertainty estimation and spatial regularization.Patterns (New York, N.Y.) · 2024Article
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2 authors.
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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.
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