Searching journal content for articles similar to Yao et al. 35 (7): 1621.

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  1. 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
    ...). In particular, single-cell multi-omics technologies like SHARE-seq (Ma et al. 2020) can simultaneously profile transcriptomic and epigenomic data within individual cells, enabling the interrogation of cellular heterogeneity and molecular hierarchy (Cao et al. 2024). Consequently, numerous methods have emerged...
  2. Method: Graph-based self-supervised learning for repeat detection in metagenomic assembly
    • Ali Azizpour,
    • Advait Balaji,
    • Todd J. Treangen,
    • and Santiago Segarra
    Genome Res. September 2024 34: 1468-1476; Published in Advance July 19, 2024, doi:10.1101/gr.279136.124
    ...-019-13429-2 ↵Jaiswal A, Babu AR, Zadeh MZ, Banerjee D, Makedon F. 2020. A survey on contrastive self-supervised learning. Technologies 9: 2. doi:10.3390/technologies9010002 ↵Kingma DP, Ba J. 2014. Adam: a method for stochastic optimization. arXiv:1412.6980 [cs.LG]. doi:10.48550/arXiv.1412.6980 ↵Kipf TN, Welling M...
  3. 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...
  4. 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
    ...technology. Moreover, ST technologies differ significantly in sensitivity to specific genes (Hartman and Satija 2024), and varying sparsity levels of the data further complicate integration. The goal of ST batch integration is to learn a spatially aware embedding function that maps gene expression profiles...
    OPEN ACCESS ARTICLE
  5. 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...
  6. 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
    ...and missing19 gene measurements, challenges that are further compounded by the higher cost20 of spatial data compared to traditional single-cell data. To overcome this chal-21 lenge, we introduce spRefine, a deep learning framework that leverages genomic22 language models to jointly denoise and impute spatial...
    OPEN ACCESS ARTICLEACCEPTED MANUSCRIPT
  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
    ..., Fernández Navarro J, Lundeberg J. 2020. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun Biol 3: 565. doi:10.1038/s42003-020-01247-y ↵Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T, Marioni JC, Buettner F, Huber W, Stegle O. 2018. Multi-Omics...
    OPEN ACCESS ARTICLE
  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...
    OPEN ACCESS ARTICLE
  9. 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
    ...genes that are crucial for identifying rare cell types or capturing fine spatial organization, limiting their ability to detect nuanced biological signals.Given the preceding discussion, both label-based methods and imputation-based label-free approaches face limitations in capturing biological...
  10. Method: High-resolution spatial transcriptomics in fixed tissue using a cost-effective PCL-seq workflow
    • Xue Dong,
    • Mengzhu Hu,
    • Xiaonan Cui,
    • Wenjian Zhou,
    • Jingtao Cai,
    • Guangyao Mao,
    • and Weiyang Shi
    Genome Res. September 2025 35: 2052-2063; Published in Advance August 5, 2025, doi:10.1101/gr.279906.124
    ...@shu.edu.cnAbstractThe spatial heterogeneity of gene expression has driven the development of diverse spatial transcriptomics technologies. Here, we present photocleavage and ligation sequencing (PCL-seq), a spatial indexing method utilizing a light-controlled DNA labeling strategy applied to tissue sections. PCL-seq employs...
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