Searching journal content for articles similar to Shu et al. 35 (10): 2285.

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  1. 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
    ...in Healthcare, Johns Hopkins University, Baltimore, Maryland 21218, USA Corresponding author: shicks19@jhu.eduAbstractRecent advances in spatially resolved single-omic and multi-omics technologies have led to the emergence of computational tools to detect and predict spatial domains. Additionally, histological...
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  2. 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
    ...to reconstruct a latent spatial gene expression matrix from a pair of observations from different SRT technologies. SIID leverages a spatial alignment and uses a joint nonnegative factorization model to accurately impute missing gene expression and infer gene expression signatures of cell types from admixed SRT...
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  3. Method: Statistically rigorous and computationally efficient chromatin stripe detection with Quagga
    • Fan Feng,
    • Sean Moran,
    • Anders S. Hansen,
    • Xiaotian Zhang,
    • and Jie Liu
    Genome Res. January 2026 36: 159-168; Published in Advance November 12, 2025, doi:10.1101/gr.280132.124
    ...signal based on its neighbors.Quagga efficiently detects stripes across diverse cell types, sequencing depths, resolutions, and 3C methodsWe applied Quagga to call stripes from Hi-C contact maps of different cell lines. Quagga found 4133 stripes for GM12878 at 10 kb resolution, 10,398 for H1, 2908 for K...
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  4. 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...
  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
    ...leverages the assumption that cells that are both spatially and transcriptionally similar share comparable true gene expression patterns.Feature alignment using a single-cell foundation modelOnce ST sparsity is aligned to match the scRNA-seq reference and the data is denoised, we proceed with feature...
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  6. Method: scSHEFT enables multiomics label transfer from scRNA-seq to scATAC-seq through dual alignment
    • Zhitao Huang,
    • Ruiqing Zheng,
    • Pengzhen Jia,
    • Xuhua Yan,
    • Jinmiao Chen,
    • and Min Li
    Genome Res. February 2026 36: 387-396; Published in Advance January 20, 2026, doi:10.1101/gr.280410.125
    ...information. To achieve a balance between interomics alignment and intraomics heterogeneity, we propose a dual alignment strategy. Specifically, scSHEFT employs an anchor-based approach to align interomics anchor pairs and a contrastive-based strategy to preserve cellular heterogeneity within each omics layer...
  7. Method: Robust and efficient annotation of cell states through gene signature scoring
    • Laure Ciernik,
    • Agnieszka Kraft,
    • Florian Barkmann,
    • Josephine Yates,
    • and Valentina Boeva
    Genome Res. March 2026 36: 630-644; Published in Advance February 18, 2026, doi:10.1101/gr.280926.125
    ...phenotype (Nevins and Potti 2007). One key application of signature scoring is cell annotation, as it offers a highly efficient and reliable method for classifying cells into types and states (Neftel et al. 2019). Notably, the quality of gene signatures plays a critical role in this process, as the accuracy...
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  8. Method: Integration of high-throughput proteomic data and complementary omics layers with PriOmics
    • Robin Kosch,
    • Katharina Limm,
    • Annette M. Staiger,
    • Nadine S. Kurz,
    • Nicole Seifert,
    • Bence Oláh,
    • Stefan Solbrig,
    • Viola Poeschel,
    • Gerhard Held,
    • Marita Ziepert,
    • Norbert Schmitz,
    • Emil Chteinberg,
    • Reiner Siebert,
    • Rainer Spang,
    • Helena U. Zacharias,
    • German Ott,
    • Peter J. Oefner,
    • and Michael Altenbuchinger
    Genome Res. January 2026 36: 197-213; Published in Advance November 18, 2025, doi:10.1101/gr.279487.124
    ...that enables the integration of diverse data modalities that can distinguish direct from indirect dependencies. To establish a PriOmics model, the user has to provide two full data matrices, one that contains the measurements of all continuous variables (e.g., protein and gene expression levels) and a matched...
  9. 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
    ...heterogeneity and the mechanisms underlying development and disease. However, current GRN inference methods fail to utilize multi-omics data and prior knowledge from a biologically interpretable insight. Therefore, we propose PRISM-GRN, a Bayesian model that seamlessly incorporates known GRNs, along with sc...
  10. Method: spRefine denoises and imputes spatial transcriptomic data 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 ; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
    ...features, for example, marker genes (Liu et al. 2025b) for different conditions. The robust features can also transfer the knowledge from the spatial transcriptomic domain to other omics-type data, for example, predicting the survival information based on the RNA-seq data from The Cancer Genome Atlas (TCGA...
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