Searching journal content for articles similar to Lin et al. 34 (1): 119.

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  1. Method: Multicondition and multimodal temporal profile inference during mouse embryonic development
    • Ran Zhang,
    • Chengxiang Qiu,
    • Galina N. Filippova,
    • Gang Li,
    • Jay Shendure,
    • Jean-Philippe Vert,
    • Xinxian Deng,
    • William Stafford Noble,
    • and Christine M. Disteche
    Genome Res. October 2025 35: 2339-2351; Published in Advance August 14, 2025, doi:10.1101/gr.279997.124
    ...) and data modalities (e.g., scRNA-seq, scATAC-seq), which makes modeling challenging. Here, we propose a joint modeling framework, Sunbear, for integrating multicondition and multimodal single-cell profiles across time. Sunbear can be used to impute single-cell temporal profile changes, align multi–data set...
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  2. Method: Quantifying pathological progression from single-cell transcriptomic data with scPSS
    • Samin Rahman Khan,
    • M Saifur Rahman,
    • M. Sohel Rahman,
    • and Md Abul Hassan Samee
    Genome Res. February 2026 36: 375-386; Published in Advance January 14, 2026, doi:10.1101/gr.280411.125
    ...principal component (PC) embeddings of gene expression are popular for single-cell analysis, with distances in PC space being used to cluster cells into groups (Fa et al. 2021) and measure differences between these groups (Nicol et al. 2024). Furthermore, contrastive methods (Abid et al. 2018; Gorla et al...
  3. 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...
  4. Method: Recovering gene regulatory networks in single-cell multi-omics data with PRISM-GRN
    • Wenhao Zhang,
    • Lan Cao,
    • Xiaoxuan Gu,
    • Yongyu Long,
    • and Ying Wang
    Genome Res. January 2026 36: 142-158; Published in Advance October 9, 2025, doi:10.1101/gr.280757.125
    ...and target genes (Gao et al. 2023).Single-cell RNA sequencing (scRNA-seq) enables gene expression profiling at the individual cell level, revealing cellular heterogeneity with single-cell resolution and significantly enhancing the understanding of cell type–specific gene regulation (Chen and Liu 2022; Kartha...
  5. Resource: Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise
    • Aliza B. Rubenstein,
    • Gregory R. Smith,
    • Zidong Zhang,
    • Xi Chen,
    • Toby L. Chambers,
    • Frederique Ruf-Zamojski,
    • Natalia Mendelev,
    • Wan Sze Cheng,
    • Michel Zamojski,
    • Mary Anne S. Amper,
    • Venugopalan D. Nair,
    • Andrew R. Marderstein,
    • Stephen B. Montgomery,
    • Olga G. Troyanskaya,
    • Elena Zaslavsky,
    • Todd Trappe,
    • Scott Trappe,
    • and Stuart C. Sealfon
    Genome Res. July 2025 35: 1664-1677; Published in Advance May 20, 2025, doi:10.1101/gr.280051.124
    ...)-expressing fibro-adipogenic progenitor cells. Single-cell regulatory circuit triad reconstruction (transcription factor, chromatin interaction site, regulated gene) also identifies largely distinct gene regulatory circuits modulated by exercise in the three muscle fiber types and LUM-expressing fibro...
  6. 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
    ...for Imaging Science, Johns Hopkins University, Baltimore, Maryland 21218, USA Corresponding author: awang87@jhu.eduAbstractThe selection of marker gene panels is critical for capturing the cellular and spatial heterogeneity in the expanding atlases of single-cell RNA sequencing (scRNA-seq) and spatial...
  7. Resource: Common and specific gene regulatory programs in zebrafish caudal fin regeneration at single-cell resolution
    • Yujie Chen,
    • Yiran Hou,
    • Qinglin Zeng,
    • Irene Wang,
    • Meiru Shang,
    • Kwangdeok Shin,
    • Christopher Hemauer,
    • Xiaoyun Xing,
    • Junsu Kang,
    • Guoyan Zhao,
    • and Ting Wang
    Genome Res. January 2025 35: 202-218; Published in Advance January 14, 2025, doi:10.1101/gr.279372.124
    ..., which is characterized by several waves of gene upregulation and downregulation. We identified and in vivo validated cell-type-specific and position-specific regeneration-responsive enhancers and constructed regulatory networks by cell type and stage. Our single-cell resolution transcriptomic...
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  8. 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
    .... 2021). Even though high-resolution spatial transcriptome technologies have been developed, such technologies show significantly lower cell segmentation resolution and the number of detected genes compared with single-cell transcriptome data. Therefore, integrating single-cell and spatial transcriptomic...
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  9. Method: TFcomb identifies transcription factor combinations for cellular reprogramming based on single-cell multiomics data
    • Chen Li,
    • Sijie Chen,
    • Yixin Chen,
    • Haiyang Bian,
    • Minsheng Hao,
    • Lei Wei,
    • and Xuegong Zhang
    Genome Res. June 2025 35: 1429-1439; Published in Advance April 10, 2025, doi:10.1101/gr.279955.124
    ...of finding reprogramming TFs and their combinations as an inverse problem, and used Tikhonov regularization to guarantee the generalization ability of solutions. For the coefficient matrix of the model, we designed a graph attention network to augment gene regulatory networks built with single-cell RNA...
  10. Method: A unified analysis of atlas single-cell data
    • Hao Chen,
    • Nam D. Nguyen,
    • Matthew Ruffalo,
    • and Ziv Bar-Joseph
    Genome Res. May 2025 35: 1219-1233; Published in Advance February 18, 2025, doi:10.1101/gr.279631.124
    ..., providing a foundation for gene- or pathway-centric analyses in multimodal, multitissue, single-cell data. GIANT is not designed for cell embeddings. The embedding is encouraged to place gene nodes representing the same gene together. This cannot be directly applied to cells because there is no concept...
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