Searching journal content for articles similar to Lin et al. 32 (10): 1906.

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  1. Method: Polyomino reconstructs spatial transcriptomic profiles with single-cell resolution via a region-allocation method
    • Quanyou Cai,
    • Lihui Lin,
    • Xin Liu,
    • and Jiekai Chen
    Genome Res. November 2025 35: 2513-2526; Published in Advance October 22, 2025, doi:10.1101/gr.280532.125
    ...stripes and vertical stripes, or spatial clustering domains. Polyomino first maps cells to bins. In this process, the different regionalization layers are used to jointly constrain the spatial localization of single cells, and each cell is assigned to the bins with the highest mapping probability. Given...
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  2. Method: Complex hierarchical structures in single-cell genomics data unveiled by deep hyperbolic manifold learning
    • Tian Tian,
    • Cheng Zhong,
    • Xiang Lin,
    • Zhi Wei,
    • and Hakon Hakonarson
    Genome Res. February 2023 33: 232-246; Published in Advance February 27, 2023, doi:10.1101/gr.277068.122
    ...resolution.To address these issues, we propose a model-based deep hyperbolic manifold learning approach: single-cell deep hierarchical map (scDHMap) (Fig. 1). To characterize the overdispersed and zero-inflated count matrix of the scRNA-seq data, we apply a zero-inflated negative binomial (ZINB) model-based...
  3. Method: A deconvolution framework that uses single-cell sequencing plus a small benchmark data set for accurate analysis of cell type ratios in complex tissue samples
    • Shuai Guo,
    • Xiaoqian Liu,
    • Xuesen Cheng,
    • Yujie Jiang,
    • Shuangxi Ji,
    • Qingnan Liang,
    • Andrew Koval,
    • Yumei Li,
    • Leah A. Owen,
    • Ivana K. Kim,
    • Ana Aparicio,
    • Sanghoon Lee,
    • Anil K. Sood,
    • Scott Kopetz,
    • John Paul Shen,
    • John N. Weinstein,
    • Margaret M. DeAngelis,
    • Rui Chen,
    • and Wenyi Wang
    Genome Res. January 2025 35: 147-161; Published in Advance November 25, 2024, doi:10.1101/gr.278822.123
    ...Department of Statistics, University of California at Riverside, Riverside, CA 92521, USA; ↵16Division of Ophthalmology, Department of Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA Corresponding author: wwang7@mdanderson.orgAbstractBulk deconvolution with single-cell...
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  4. Method: spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model
    • Tianyu Liu,
    • Tinglin Huang,
    • Wengong Jin,
    • Tinyi Chu,
    • Rex Ying,
    • and Hongyu Zhao
    Genome Res. February 3, 2026 ; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
    ...that are further compounded by the higher cost of spatial data compared to traditional single-cell data. To overcome this challenge, we introduce spRefine, a deep learning framework that leverages genomic language models to jointly denoise and impute spatial transcriptomic data. Our results demonstrate that sp...
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  5. Method: Dissecting multilayer cell–cell communications with signaling feedback loops from spatial transcriptomics data
    • Lulu Yan,
    • Jinyu Cheng,
    • Qing Nie,
    • and Xiaoqiang Sun
    Genome Res. June 2025 35: 1400-1414; Published in Advance April 22, 2025, doi:10.1101/gr.279857.124
    ...). In addition, CytoSignal (Liu et al. 2024) and DeepTalk (Yang et al. 2024) decipher spatial CCC at single-cell resolution. Although these methods provide promising advancement in CCC inference, the elaborate mechanisms underlying CCC have not been fully exploited. In fact, the signaling mechanisms within CCC...
  6. Method: A novel multislice framework for precision 3D spatial domain reconstruction and disease pathology analysis
    • Daijun Zhang,
    • Ren Qi,
    • Xun Lan,
    • and Bin Liu
    Genome Res. August 2025 35: 1794-1808; Published in Advance July 14, 2025, doi:10.1101/gr.280281.124
    ...-dimensional (3D) positions of spots and constructing a global 3D structure. (2) Microenvironmental deconstruction: trains a cell-to-spot mapping matrix M using spatial transcriptomic (ST) gene expression data (Xst) and single-cell data (Xsc) via a spatially informed contrastive learning model. In this model...
  7. Method: Sequencing of individual barcoded cDNAs using Pacific Biosciences and Oxford Nanopore Technologies reveals platform-specific error patterns
    • Alla Mikheenko,
    • Andrey D. Prjibelski,
    • Anoushka Joglekar,
    • and Hagen U. Tilgner
    Genome Res. April 2022 32: 726-737; Published in Advance March 17, 2022, doi:10.1101/gr.276405.121
    ...al. 2018; Singh et al. 2019), which yields single-cell and spatially resolved long-read transcriptomes. Various platforms such as Pacific Biosciences (PacBio) (Eid et al. 2009; Koren et al. 2012; Au et al. 2013; Sharon et al. 2013; Tilgner et al. 2014; Weirather et al. 2015), Oxford Nanopore...
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