Winnow-KAN: single-cell RNA-seq location recovery with small-gene-set spatial transcriptomics
Abstract Keywords: Cell mapping, Deep Learning, Kolmogorov-Arnold network, Single-cell RNA-seq, Spatial transcriptomics. Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity. However, its collection process prevents the investigation of tissue organiz...
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2025-08-01
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| Online Access: | https://doi.org/10.1186/s12859-025-06243-9 |
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| author | Yuyang Zhang Qihuang Zhang |
| author_facet | Yuyang Zhang Qihuang Zhang |
| author_sort | Yuyang Zhang |
| collection | DOAJ |
| description | Abstract Keywords: Cell mapping, Deep Learning, Kolmogorov-Arnold network, Single-cell RNA-seq, Spatial transcriptomics. Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity. 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. |
| format | Article |
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| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
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| spelling | doaj-art-8c83fc39ca17453695ce9ea296aee2ea2025-08-20T04:02:42ZengBMCBMC Bioinformatics1471-21052025-08-0126111710.1186/s12859-025-06243-9Winnow-KAN: single-cell RNA-seq location recovery with small-gene-set spatial transcriptomicsYuyang Zhang0Qihuang Zhang1Department of Epidemiology, Biostatistics and Occupational Health, McGill UniversityDepartment of Epidemiology, Biostatistics and Occupational Health, McGill UniversityAbstract Keywords: Cell mapping, Deep Learning, Kolmogorov-Arnold network, Single-cell RNA-seq, Spatial transcriptomics. Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity. 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.https://doi.org/10.1186/s12859-025-06243-9Cell mappingDeep learningKolmogorov-Arnold networkSingle-cell RNA-seqSpatial transcriptomics |
| spellingShingle | Yuyang Zhang Qihuang Zhang Winnow-KAN: single-cell RNA-seq location recovery with small-gene-set spatial transcriptomics BMC Bioinformatics Cell mapping Deep learning Kolmogorov-Arnold network Single-cell RNA-seq Spatial transcriptomics |
| title | Winnow-KAN: single-cell RNA-seq location recovery with small-gene-set spatial transcriptomics |
| title_full | Winnow-KAN: single-cell RNA-seq location recovery with small-gene-set spatial transcriptomics |
| title_fullStr | Winnow-KAN: single-cell RNA-seq location recovery with small-gene-set spatial transcriptomics |
| title_full_unstemmed | Winnow-KAN: single-cell RNA-seq location recovery with small-gene-set spatial transcriptomics |
| title_short | Winnow-KAN: single-cell RNA-seq location recovery with small-gene-set spatial transcriptomics |
| title_sort | winnow kan single cell rna seq location recovery with small gene set spatial transcriptomics |
| topic | Cell mapping Deep learning Kolmogorov-Arnold network Single-cell RNA-seq Spatial transcriptomics |
| url | https://doi.org/10.1186/s12859-025-06243-9 |
| work_keys_str_mv | AT yuyangzhang winnowkansinglecellrnaseqlocationrecoverywithsmallgenesetspatialtranscriptomics AT qihuangzhang winnowkansinglecellrnaseqlocationrecoverywithsmallgenesetspatialtranscriptomics |