{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T10:05:39Z","timestamp":1771927539136,"version":"3.50.1"},"reference-count":238,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3389\/fbinf.2025.1715821","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T06:40:47Z","timestamp":1769496047000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges"],"prefix":"10.3389","volume":"5","author":[{"given":"Boris","family":"Tchatchoua Ngassam","sequence":"first","affiliation":[{"name":"Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario","place":["London, ON, Canada"]}]},{"given":"Huilin","family":"Niu","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario","place":["London, ON, Canada"]}]},{"given":"Sunny","family":"Pang","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario","place":["London, ON, Canada"]}]},{"given":"Valeryia","family":"Shydlouskaya","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Immunology, Schulich School of Medicine and Dentistry, University of Western Ontario","place":["London, ON, Canada"]}]},{"given":"Tallulah S.","family":"Andrews","sequence":"additional","affiliation":[{"name":"Department of Biochemistry, Schulich School of Medicine and Dentistry, University of Western Ontario","place":["London, ON, Canada"]},{"name":"Department of Computer Science, University of Western Ontario","place":["London, ON, Canada"]}]}],"member":"1965","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1186\/s13059-019-1795-z","article-title":"A comparison of automatic cell identification methods for single-cell RNA sequencing data","volume":"20","author":"Abdelaal","year":"2019","journal-title":"Genome Biol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1038\/s41592-019-0576-7","article-title":"Exploring single-cell data with deep multitasking neural networks","volume":"16","author":"Amodio","year":"2019","journal-title":"Nat. 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