ArticleBriefings in bioinformatics2024
SpaDiT: diffusion transformer for spatial gene expression prediction using scRNA-seq.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- DiTSim: A Diffusion-Transformers Based Single-Cell ATAC-seq Data Simulator.Interdisciplinary sciences, computational life sciences · 2026Article
- Review
- Accurate prediction in reconstructed spatial transcriptomes does not ensure valid biological discovery.bioRxiv : the preprint server for biology · 2026Article
- scGenoByte: a GenoByte embedding transformer with biological priors for cell type annotation.Briefings in bioinformatics · 2026Article
- PAIR: Reconstructing Single-Cell Open-Chromatin Landscapes for Transcription Factor Regulome Mapping.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Hypergraph representations of single-cell RNA sequencing data for improved cell clustering.Bioinformatics (Oxford, England) · 2026Article
- S3RL: Enhancing Spatial Single-Cell Transcriptomics With Separable Representation Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SpaLSTF: Diffusion-based generative model with BiLSTM and XCA-Transformer for spatial transcriptomics imputation.PLoS computational biology · 2026Article
- S2potAE: multimodal spatial spot autoencoder integrating image and transcriptomic features for deconvolution.Briefings in bioinformatics · 2026Article
- So3D: a comprehensive three-dimensional spatial omics resource for decoding tissue architecture in physiology and disease.Nucleic acids research · 2026Article
- SpateCV: cross-modality alignment regularization of cell types improves spatial gene imputation for spatial transcriptomics.Journal of translational medicine · 2025Article
- SpaCross deciphers spatial structures and corrects batch effects in multi-slice spatially resolved transcriptomics.Communications biology · 2025Article
- SpaIM: single-cell spatial transcriptomics imputation via style transfer.Nature communications · 2025Article
- DANet: spatial gene expression prediction from H&E histology images through dynamic alignment.Briefings in bioinformatics · 2025Article
- SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics.PLoS computational biology · 2025Article
- Emerging AI approaches for cancer spatial omics.GigaScience · 2025Review
- Linking transcriptome and morphology in bone cells at cellular resolution with generative AI.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Article
- Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location, and histology using GCN.Briefings in bioinformatics · 2024Article
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3 authors.
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Abstract
The rapid development of spatially resolved transcriptomics (SRT) technologies has provided unprecedented opportunities for exploring the structure of specific organs or tissues. However, these techniques (such as image-based SRT) can achieve single-cell resolution, but can only capture the expression levels of tens to hundreds of genes. Such spatial transcriptomics (ST) data, carrying a large number of undetected genes, have limited its application value. To address the challenge, we develop SpaDiT, a deep learning framework for spatial reconstruction and gene expression prediction using scRNA-seq data. SpaDiT employs scRNA-seq data as an a priori condition and utilizes shared genes between ST and scRNA-seq data as latent representations to construct inputs, thereby facilitating the accurate prediction of gene expression in ST data. SpaDiT enhances the accuracy of spatial gene expression predictions over a variety of spatial transcriptomics datasets. We have demonstrated the effectiveness of SpaDiT by conducting extensive experiments on both seq-based and image-based ST data. We compared SpaDiT with eight highly effective baseline methods and found that our proposed method achieved an 8%-12% improvement in performance across multiple metrics. Source code and all datasets used in this paper are available at https://github.com/wenwenmin/SpaDiT and https://zenodo.org/records/12792074.
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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.