Searching journal content for articles similar to Li et al. 33 (10): 1757.

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  1. 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
    ...Torch-lightning Falcon497 (2019) to construct and train the model. Our default optimizer is Adam Kinga et al498 (2015), with a learning rate of 1E -4. Our optimal batch size and latent dimensions499 are tuned for dataset-specific settings, while the batch size is set as large as possible500 by default, which is shown...
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  2. Method: Early feature extraction drives model performance in high-resolution chromatin accessibility prediction
    • Aayush Grover,
    • Till Muser,
    • Liine Kasak,
    • Lin Zhang,
    • Ekaterina Krymova,
    • and Valentina Boeva
    Genome Res. March 2026 36: 619-629; Published in Advance January 12, 2026, doi:10.1101/gr.281042.125
    ...that early feature extraction, rather than downstream architecture, is the primary determinant of prediction accuracy. A comprehensive evaluation of these models on ATAC-seq signal prediction at 4-bp resolution in a cell type–specific manner identifies the ConvNeXt-based dilated CNN as the most robust...
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  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
    ...Mix (Chidester et al. 2023). SpiceMix models gene expression as a combination of latent factors (“metagenes”) that capture transcriptional programs with spatial organization. Joint analysis enables a more comprehensive understanding of tissue biology by revealing conserved spatial patterns that may be missed...
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  4. 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
    ...(duVerle et al. 2016; Lopez et al. 2018; Sun et al. 2018; Grønbech et al. 2020; Armingol et al. 2024) build statistical models of gene expression in which cell types and marker genes enter as latent variables. They typically rely on heavy computation and are not easily scalable. Label-based methods...
  5. 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
    ...the latent space is by finding a low-dimensional factorization of scRNA-seq gene expression matrices (Yang and Michailidis 2016; Argelaguet et al. 2018, 2020; Townes et al. 2019; Liu et al. 2020; Qian et al. 2022).Similarly, in the spatial transcriptomics domain, the SRT gene expression is traditionally...
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  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
    ...with a precorrection mechanism (STMSC). STMSC integrates slice alignment via the H&E image and performs spot deconvolution while simultaneously constructing the latent space for spatial domain identification, aiming to better reflect the organizational structure of spatial domains. Notably, the STMSC framework...
  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
    .... Recent advancements in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) technologies have empowered the comprehensive characterization of gene programs at both single-cell and spatial resolutions. Here, we present DeCEP, a computational framework designed to characterize context...
  8. Method: EnDeep4mC predicts DNA N4-methylcytosine sites using a dual-adaptive feature encoding framework in deep ensembles
    • Shuyu Zhang,
    • Quan Zou,
    • Mengting Niu,
    • Zhibin Lv,
    • Antony Stalin,
    • and Ximei Luo
    Genome Res. March 2026 36: 589-599; Published in Advance February 17, 2026, doi:10.1101/gr.280977.125
    ...). The Transformer relied on the self-attention mechanism to strengthen the global correlation discrimination, which significantly improved the prediction accuracy of class imbalance samples. In summary, by integrating these heterogeneous base models, EnDeep4mC dynamically adapted to multiscale sequence features...
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  9. 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...
  10. 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
    ...clustering framework for spatial transcriptomics data that aggregates outcomes from state-of-the-art tools using a variety of consensus strategies, including Onehot-based, average-based, hypergraph-based, and wNMF-based methods. Comprehensive assessments on simulated and real data from distinct experimental...
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