AUTHOR=Tchatchoua Ngassam Boris , Niu Huilin , Pang Sunny , Shydlouskaya Valeryia , Andrews Tallulah S. 
  
TITLE=Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges
  
JOURNAL=Frontiers in Bioinformatics
  
VOLUME=Volume 5 - 2025
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2025.1715821
  
DOI=10.3389/fbinf.2025.1715821
  
ISSN=2673-7647
  
ABSTRACT=Artificial intelligence (AI) has become a common tool for bioinformatics, with hundreds of methods published in recent years. Due to the training data demands of deep-learning algorithms, high-throughput single-cell and spatial transcriptomics is one of the most popular areas for these applications. Here we review how AI is being used for single-cell and spatial transcriptomics analysis, and how these approaches compare to alternative statistical or heuristic-based methods. We explored 10 common analysis tasks: dimensionality reduction, cross-dataset integration, data denoising, data augmentation, deconvolution, cell-cell interactions, transcriptional velocity, transcriptomic-chromatin accessibility integration, and integrating single-cell and spatial transcriptomics modalities. We highlight which algorithms are likely to be useful for discovery researchers, and which are not yet ready for general research use.