ArticleNature communications2025
SpaIM: single-cell spatial transcriptomics imputation via style transfer.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Enhancing RNA Capture Efficiency in Spatial Transcriptomics: A Review of Innovative Technologies and Strategies.International journal of molecular sciences · 2025Pooled it
- Microenvironment-aware transcriptome reconstruction in spatial transcriptomics.Nature communications · 2026Article
- Enhancing pan-cancer spatial transcriptomics at single-cell resolution with stPainter.Nature communications · 2026Article
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- R4ST: a reference-guided graph-generative model for robust reconstruction of spatial transcriptomic profiles.Bioinformatics (Oxford, England) · 2026Article
- Review
- Cross-modality representation and multi-sample integration of spatially resolved omics data.Briefings in bioinformatics · 2026Article
- A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis.bioRxiv : the preprint server for biology · 2026Article
- SpaJoint: a transfer learning method for spatial transcriptomics deconvolution.Briefings in bioinformatics · 2026Article
- S3RL: Enhancing Spatial Single-Cell Transcriptomics With Separable Representation Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Spotting a unicorn: spatial transcriptome analysis of the eyelid reveals gene regulatory networks enriched in Moll glands.Briefings in bioinformatics · 2026Article
- The good, the bad, and the ugly: opportunities, challenges, and pitfalls in spatial proteomics modeling.Briefings in bioinformatics · 2026Review
- SpatialDG: a novel spatial domain identification method for spatially resolved transcriptomics data based on dual-graph neural network.Briefings in bioinformatics · 2026Article
- SpaLSTF: Diffusion-based generative model with BiLSTM and XCA-Transformer for spatial transcriptomics imputation.PLoS computational biology · 2026Article
- VISTA uncovers missing gene expression and spatial-induced information for spatial transcriptomic data analysis.Communications biology · 2026Article
- PathCLAST: pathway-augmented contrastive learning with attention for interpretable spatial transcriptomics.Briefings in bioinformatics · 2026Article
- AugGCL: Multimodal graph learning for spatial transcriptomics analysis with enhanced gene and morphological data.PLoS computational biology · 2026Article
- Benign Uroandrological Ecosystem: A Thorough Overview from Single-Cell and Spatial Transcriptomics.Research (Washington, D.C.) · 2026Review
- Cracking PRMT5: Mechanistic insights, clinical advances, and AI-driven strategies.Cancer letters · 2025Review
- SpaTM: topic models for inferring spatially informed transcriptional programs.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Spatial transcriptomics (ST) technologies have transformed our understanding of cellular organization but are limited by sparse signals and restricted gene coverage. To address these challenges, we introduce SpaIM, a style transfer learning model that leverages single-cell RNA sequencing (scRNA-seq) data to predict unmeasured gene expressions in ST profiles. By disentangling shared content and modality-specific styles, SpaIM effectively integrates scRNA-seq's rich gene expression with the spatial context of ST. Evaluated across 53 datasets spanning sequencing- and imaging-based spatial technologies in various tissue types, SpaIM consistently outperforms 12 state-of-the-art methods in improving gene coverage and expression accuracy. Furthermore, SpaIM enhances downstream analyses, including ligand-receptor interaction inference, spatial domain characterization, and differential gene expression analysis. Released as open-source software, SpaIM expands accessibility and utility in ST research. Overall, SpaIM represents a robust and generalizable framework for enriching ST data with single-cell information, enabling deeper insights into tissue architecture and cellular function.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.