Searching journal content for articles similar to Liu et al. 32 (9): 1736.

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  1. Method: Scalable cell-specific coexpression networks for granular regulatory pattern discovery with NeighbourNet
    • Yidi Deng,
    • Jiadong Mao,
    • Jarny Choi,
    • and Kim-Anh Le Cao
    Genome Res. March 5, 2026 gr.281171.125; Published in Advance March 5, 2026, doi:10.1101/gr.281171.125
    ...1Scalable cell-specific coexpression networks for granular regulatory 2 pattern discovery with NeighbourNet 3 Yidi Deng1,2, Jiadong Mao1,† & Jarny Choi3,† & Kim-Anh Lê Cao1,*,† 4 1Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, 5 3010, Australia 6...
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  2. Method: Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies
    • Jianing Yao,
    • Jinglun Yu,
    • Brian Caffo,
    • Stephanie C. Page,
    • Keri Martinowich,
    • and Stephanie C. Hicks
    Genome Res. July 2025 35: 1621-1632; Published in Advance May 20, 2025, doi:10.1101/gr.279380.124
    ...how our method is computationally efficient and scalable in terms of memory and time, and we provide open-source software implemented in Python.ResultsOverview of Proust to detect spatial domains integrating multiple data modalitiesProust is a graph-based contrastive self-supervised learning framework...
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  3. Method: Kernel-bounded clustering for spatial transcriptomics enables scalable discovery of complex spatial domains
    • Hang Zhang,
    • Yi Zhang,
    • Kai Ming Ting,
    • Jie Zhang,
    • and Qiuran Zhao
    Genome Res. February 2025 35: 355-367; Published in Advance February 5, 2025, doi:10.1101/gr.278983.124
    ...PCA, Stagate, and stLearn plotted on DLPFC slice 151669.Studies on simulated ST dataSRTsim, which is developed by Zhu et al. (2023), is a spatial pattern preserving simulator for scalable, reproducible, and realistic SRT simulations. Here we apply SRTsim to generate simulated data sets. The LIBD human DLPFC...
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  4. Method: Efficient integration of spatial omics data for joint domain detection, matching, and alignment with stMSA
    • Han Shu,
    • Jing Chen,
    • Chang Xu,
    • Jialu Hu,
    • Yongtian Wang,
    • Jiajie Peng,
    • Qinghua Jiang,
    • Xuequn Shang,
    • and Tao Wang
    Genome Res. October 2025 35: 2285-2299; Published in Advance August 14, 2025, doi:10.1101/gr.280584.125
    ...transcriptomics with other omics layers. Here, we introduce spatial multislice/omics analysis (stMSA), a deep graph contrastive learning model that incorporates graph auto-encoder techniques. stMSA is specifically designed to produce batch-corrected representations while retaining the distinct spatial patterns...
  5. Method: STCC enhances spatial domain detection through consensus clustering of spatial transcriptomics data
    • Congcong Hu,
    • Nana Wei,
    • Jiyuan Yang,
    • Hua-Jun Wu,
    • and Xiaoqi Zheng
    Genome Res. June 2025 35: 1415-1428; Published in Advance May 12, 2025, doi:10.1101/gr.280031.124
    ...technologies has yielded substantial spatial transcriptomics data. Deriving biological insights from these data poses nontrivial computational and analysis challenges, of which the most fundamental step is spatial domain detection (or spatial clustering). Although a number of tools for spatial domain detection...
  6. Method: A scalable computational framework for predicting gene expression from candidate cis-regulatory elements
    • Qinhu Zhang,
    • Siguo Wang,
    • Zhipeng Li,
    • Wenzheng Bao,
    • Wenjian Liu,
    • and De-Shuang Huang
    Genome Res. February 2026 36: 361-374; Published in Advance January 16, 2026, doi:10.1101/gr.281219.125
    ..., and epigenomic tracks from discrete cCREs. A series of comparative experiments demonstrates the superiority and scalability of ScPGE. By comprehensively analyzing ScPGE's predictions, we identified distinct regulatory patterns in TPs, FPs, TNs, and FNs, particularly a pattern in TPs where the regulatory effect...
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  7. Method: Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms
    • Hongyu Zheng,
    • Hirak Sarkar,
    • and Benjamin J. Raphael
    Genome Res. December 2025 35: 2734-2743; Published in Advance November 17, 2025, doi:10.1101/gr.280555.125
    ...that lead to microvasculature in breast cancer (Zhu et al. 2022). Thus, we refer to this cluster as adipocyte-rich stroma. Spatial colocalization (Fig. 5A) of these cell subtypes shows a clear pattern of stromal subtypes where myCAF cells localize in the interior of iCAFs that constitute the majority...
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  8. Method: Unified integration of spatial transcriptomics across platforms with LLOKI
    • Ellie Haber,
    • Ajinkya Deshpande,
    • Jian Ma,
    • and Spencer Krieger
    Genome Res. December 2025 35: 2722-2733; Published in Advance November 13, 2025, doi:10.1101/gr.280803.125
    ...challenges in ST data integration, LLOKI lays the groundwork for scalable, cross-technology spatial transcriptomics analysis, empowering researchers to uncover biologically meaningful patterns across diverse tissue samples and experimental conditions.MethodsLLOKI is a framework for integrating spatial...
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  9. Method: spRefine denoises and imputes spatial transcriptomics 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 gr.281001.125; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
    ...port Peyre´ et al (2019) across spatial distributions to capture the dynamic process of228 gene expression changes and infer CCCs. According to Figure 3 (A), a higher num-229 ber of CCCs (19 vs. 3) were inferred based on the imputed profiles, as expected. By230 detecting the CCCs based on sc...
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  10. Method: Partitioned multi-MUM finding for scalable pangenomics with MumemtoM
    • Vikram S. Shivakumar and
    • Ben Langmead
    Genome Res. February 2026 36: 397-404; Published in Advance November 7, 2025, doi:10.1101/gr.280940.125
    ...Vikram S. Shivakumar and Ben Langmead Department of Computer Science, Johns Hopkins University, Baltimore, Maryland 21218, USA Corresponding authors: vshivak1@jhu.edu, langmea@cs.jhu.eduAbstractPan collections are growing to hundreds of high-quality s. This necessitates scalable methods...
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