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However, its collection process prevents the investigation of tissue organization due to the lack of spatial origins for the cells. Recent advances in computational methods have addressed this gap by leveraging spatial transcriptomics, which simultaneously profiles gene expression and spatial coordinates. While these state-of-the-art methods demonstrate excellent performance in cell location recovery, their effectiveness is often specific to the particular pair of scRNA-seq and spatial transcriptomics datasets used, limiting their scalability to larger datasets and generalizability to external query scRNA-seq data. In this study, we demonstrate the feasibility of leveraging a novel model architecture to address the redundancy in scRNA-seq datasets and facilitate prediction with a much smaller set of genes. We present Winnow-KAN, a method designed to reduce the number of required gene variables in cell-mapping tasks. Built on a modified structure of the Kolmogorov-Arnold Network, Winnow-KAN leverages the Kolmogorov-Arnold Representation Theorem to facilitate location predictions using fewer features than those required by multi-layer perceptron-based methods. Winnow-KAN includes a selector layer that reduces the size of the gene set used for prediction, enabling the model to recover query scRNA-seq data with performance comparable to MLP-based approaches that rely on the full gene set. We benchmarked Winnow-KAN using multiple datasets generated from brain and cancer tissues, derived from platforms such as Visium and MERFISH.<\/jats:p>","DOI":"10.1186\/s12859-025-06243-9","type":"journal-article","created":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T10:22:03Z","timestamp":1754994123000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Winnow-KAN: single-cell RNA-seq location recovery with small-gene-set spatial transcriptomics"],"prefix":"10.1186","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5363-5612","authenticated-orcid":false,"given":"Yuyang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1455-2159","authenticated-orcid":false,"given":"Qihuang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,12]]},"reference":[{"key":"6243_CR1","doi-asserted-by":"crossref","unstructured":"Andersson A, Andrusivov\u00e1 \u017d, Czarnewski P, Li X, Sundstr\u00f6m E, Lundeberg J. A landmark-based common coordinate framework for spatial transcriptomics data.  bioRxiv. 2021. https:\/\/www.biorxiv.org\/content\/early\/2021\/11\/13\/2021.11.11.468178","DOI":"10.1101\/2021.11.11.468178"},{"issue":"4","key":"6243_CR2","doi-asserted-by":"publisher","DOI":"10.1111\/bpa.13235","volume":"34","author":"V Astillero-Lopez","year":"2024","unstructured":"Astillero-Lopez V, Villar-Conde S, Gonzalez-Rodriguez M, Flores-Cuadrado A, Ubeda-Banon I, Saiz-Sanchez D, Martinez-Marcos A. Proteomic analysis identifies HSP90AA1, PTK2B, and ANXA2 in the human entorhinal cortex in Alzheimer\u2019s disease: Potential role in synaptic homeostasis and A pathology through microglial and astroglial cells. 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This study is based on publicly available or previously published data, and no new data collection involving human participants was conducted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable. This study does not include identifying images, personal data, or clinical details of participants that would require consent for publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no c onflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"209"}}