ArticleNucleic acids research2025
stAI: a deep learning-based model for missing gene imputation and cell-type annotation of spatial transcriptomics.
Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- scGSI: Graph-guided self-supervised integration of paired single-cell multi-omics.PLoS computational biology · 2026Article
- Multimodal spatial omics: From data acquisition to computational integration.Patterns (New York, N.Y.) · 2026Review
- CTMAP: an adversarial cross-modal learning framework for accurate and robust cell-type annotation in single-cell resolution spatial transcriptomics.Briefings in bioinformatics · 2026Article
- R4ST: a reference-guided graph-generative model for robust reconstruction of spatial transcriptomic profiles.Bioinformatics (Oxford, England) · 2026Article
- Review
- The expression pattern of EXO70 subunits in single-cell stereo-seq of Arabidopsis thaliana leaves using coexistence network analysis.Scientific reports · 2026Article
- SpaLSTF: Diffusion-based generative model with BiLSTM and XCA-Transformer for spatial transcriptomics imputation.PLoS computational biology · 2026Article
- Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation.Frontiers in immunology · 2026Review
- Applications and development of in situ nucleic acid visualization techniques.Frontiers in bioengineering and biotechnology · 2026Review
- Accurate imputation of pathway-specific gene expression in spatial transcriptomics with PASTA.Nature communications · 2025Article
- SpateCV: cross-modality alignment regularization of cell types improves spatial gene imputation for spatial transcriptomics.Journal of translational medicine · 2025Article
- Multidimensional decoding of colorectal cancer heterogeneity: Artificial intelligence-enabled precision exploration of single-cell and spatial transcriptomics.World journal of gastrointestinal oncology · 2025Review
- SIGEL: a context-aware genomic representation learning framework for spatial genomics analysis.Genome biology · 2025Article
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4 authors.
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Abstract
Spatial transcriptomics technology has revolutionized our understanding of cellular systems by capturing RNA transcript levels in their original spatial context. Single-cell spatial transcriptomics (scST) offers single-cell resolution expression level and precise spatial information of RNA transcripts, while it has a limited capacity for simultaneously detecting a wide range of RNA transcripts, hindering its broader applications. Characterizing the whole transcriptome level and comprehensively annotating cell types represent two significant challenges in scST applications. Despite several proposed methods for one or both tasks, their performance remains inadequate. In this work, we introduce stAI, a deep learning-based model designed to address both missing gene imputation and cell-type annotation for scST data. stAI leverages a joint embedding for the scST and the reference scRNA-seq data with two separate encoder-decoder modules. Both the imputation and annotation are performed within the latent space in a supervised manner, utilizing scRNA-seq data to guide the processes. Experiments for datasets generated from diverse platforms with varying numbers of measured genes were conducted and compared with the updated methods. The results demonstrate that stAI can predict the unmeasured genes, especially the marker genes, with much higher accuracy, and annotate the cell types, including those of small size, with high precision.
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