Searching journal content for articles similar to Zhang et al. 35 (8): 1794.

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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
    ...reduce batch effect, discover novel cell-cell121 communications, and improve spatial ageing clock modeling. Moreover, spRefine can122 also work as a pre-training framework to unify cell and spot representations. In addi-123 tion, we show that spRefine can leverage the disease-associated marker genes...
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  2. Method: Partial alignment of multislice spatially resolved transcriptomics data
    • Xinhao Liu,
    • Ron Zeira,
    • and Benjamin J. Raphael
    Genome Res. July 2023 33: 1124-1132; Published in Advance August 8, 2023, doi:10.1101/gr.277670.123
    ...multiple slices can help recover this information and improve downstream expression analyses, but multislice alignment and integration remains a challenging task. Existing methods for integrating SRT data either do not use spatial information or assume that the morphology of the tissue is largely preserved...
  3. Method: Long-read reconstruction of many diverse haplotypes with devider
    • Jim Shaw,
    • Christina Boucher,
    • Yun William Yu,
    • Noelle Noyes,
    • and Heng Li
    Genome Res. December 2025 35: 2637-2649; Published in Advance September 23, 2025, doi:10.1101/gr.280510.125
    ...55421, USA Corresponding author: jshaw@ds.dfci.harvard.eduAbstractReconstructing exact haplotypes is important when sequencing a mixture of similar sequences. Long-read sequencing can connect distant alleles to disentangle similar haplotypes, but handling sequencing errors requires specialized...
  4. 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
    ...modality. This can lead to detected spatial domains corresponding to known morphology in healthy tissue, pathologies associated with disease, or both. For instance, in the tissue slice from Br3880, Proust identified spotty areas (cluster 3) and a layer (cluster 2), which are visually correlated to Aβ...
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  5. 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
    ...spatial organization and dynamics across diverse contexts in health and disease (Hu et al. 2024). Although batch integration has been extensively studied in scRNA-seq (Argelaguet et al. 2021), ST presents unique challenges. A key difficulty is variation in gene panels across samples, even within the same...
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  6. 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...
  7. Method: Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics
    • Zhen Li,
    • Xiaoyang Chen,
    • Xuegong Zhang,
    • Rui Jiang,
    • and Shengquan Chen
    Genome Res. October 2023 33: 1757-1773; Published in Advance October 30, 2023, doi:10.1101/gr.277891.123
    ...and increase the demand for comprehensive methods to effectively characterize spatial domains. As a prerequisite for ST data analysis, spatial domain characterization is a crucial step for downstream analyses and biological implications. Here we propose a prior-based self-attention framework for spatial...
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  8. Resource: Cell type–specific gene regulatory atlas prioritizes drug targets and repurposable medicines in Alzheimer's disease
    • Yunxiao Ren,
    • Ming Hu,
    • Yang E. Li,
    • Andrew A. Pieper,
    • Jeffrey Cummings,
    • and Feixiong Cheng
    Genome Res. March 2026 36: 645-659; Published in Advance January 21, 2026, doi:10.1101/gr.280436.125
    ...degeneration, impaired postnatal neurogenesis, and synaptic dysfunction, also play crucial roles in disease progression (Ballard et al. 2011; Long and Holtzman 2019; Murdock and Tsai 2023). This intricate pathology poses significant challenges for developing effective therapies. Recent advances in single...
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  9. 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
    ...of mouse brain tissue and applied the same methods for mapping. Consistent results were obtained, further validating Polyomino's robustness and precision in spatial reconstruction (Supplemental Fig. S8).To further broaden this analysis, we systematically evaluated the performance of Polyomino...
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  10. 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
    ..., and multislice alignment for 3D tissue reconstruction. By unifying these capabilities, stMSA facilitates integrative analysis of spatially resolved multiomics data, advancing insights into complex tissue architectures.ResultsOverview of stMSAThis study introduces stMSA, a novel deep graph representation learning...
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