Searching journal content for articles similar to Zhang et al. 35 (2): 355.

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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: 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...
  3. 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
    ...scoring approach.These findings further demonstrate that LLOKI not only enables effective integration of heterogeneous spatial transcriptomics data but also facilitates the discovery of biologically meaningful gene programs, such as the one defining tumor-infiltrating T cells in ovarian cancer...
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  4. 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
    ...the analyses of several spatial transcriptomic data,431 we showed that the results produced by spRefine can better cluster cells and enable432 more effective downstream analyses.433 Given the growing volume of spatial transcriptomic data, there is the poten-434 tial to collect a large amount of data for pre...
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  5. Method: Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover
    • An Wang,
    • Stephanie Hicks,
    • Donald Geman,
    • and Laurent Younes
    Genome Res. December 2025 35: 2744-2755; Published in Advance November 12, 2025, doi:10.1101/gr.280539.125
    ...transcriptomic discoveries, focusing on its ability to uncover nuanced cell types and spatial organizations in the CBMC, mouse brain Visium HD, and scFFPE breast cancer data set. We then discuss the scalability of geneCover, showcasing its ability to efficiently handle large data sets. Following this, we analyze...
  6. 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
    ...applied to real-world 10x Xenium-Visium pairs from human breast and colon cancer tissues, SIID achieves highest performance in imputing holdout gene expression.Spatially resolved transcriptomics (SRT) technologies have transformed the study of tissue biology by enabling the simultaneous measurement...
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  7. Method: Deciphering context-specific gene programs from single-cell and spatial transcriptomics data with DeCEP
    • Lin Li,
    • Xianbin Su,
    • and Ze-Guang Han
    Genome Res. October 2025 35: 2300-2315; Published in Advance August 21, 2025, doi:10.1101/gr.279689.124
    ..., the development of spatial transcriptomics (ST) technologies has enabled the depiction of spatial coregulation patterns of gene programs within a specific tissue (Rao et al. 2021; Moses and Pachter 2022; Walker et al. 2022). However, current computational methods for systematically deciphering gene programs...
  8. Method: Harnessing agent-based frameworks in CellAgentChat to unravel cell–cell interactions from single-cell and spatial transcriptomics
    • Vishvak Raghavan,
    • Yumin Zheng,
    • Yue Li,
    • and Jun Ding
    Genome Res. July 2025 35: 1646-1663; Published in Advance May 2, 2025, doi:10.1101/gr.279771.124
    ...). Lastly, ABMs facilitate the straightforward and effective adjustment of agent rules, enabling perturbations in agent behavior.To bridge the aforementioned gaps, we introduce CellAgentChat, a comprehensive ABM framework for inferring and visualizing CCIs from scRNA-seq and spatial transcriptomics data...
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  9. 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
    ...flexible scalability from three aspects: (i) scalability of epigenomic tracks, enabling the use of any number of epigenomic tracks if available; (ii) scalability of cCREs, allowing for the incorporation of any number of cCREs if reasonable; and (iii) scalability of model structure, adjusting the layers...
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  10. 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
    ...Dissecting multilayer cell–cell communications with signaling feedback loops from spatial transcriptomics data Lulu Yan1,4, Jinyu Cheng2,4, Qing Nie3 and Xiaoqiang Sun1 1School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China; 2Zhongshan School of Medicine, Sun Yat-sen University...
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