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  1. Method: A gene regulatory network–aware graph learning method for cell identity annotation in single-cell RNA-seq data
    • Mengyuan Zhao,
    • Jiawei Li,
    • Xiaoyi Liu,
    • Ke Ma,
    • Jijun Tang,
    • and Fei Guo
    Genome Res. July 2024 34: 1036-1051; Published in Advance August 12, 2024, doi:10.1101/gr.278439.123
    ...of emulating molecular mechanisms in which genes act as “bridges” in biological systems while achieving distantly related cellular communications.Thus, scHGR is a single-cell resolution annotation tool that integrates gene regulatory networks into cell identity inference. For scRNA-seq data sets from various...
  2. Method: Simultaneous modeling of chromatin conformation changes from multiple single-cell interaction maps with ChromMovie
    • Krzysztof H. Banecki,
    • Haoxi Chai,
    • Yijun Ruan,
    • and Dariusz Plewczynski
    Genome Res. April 8, 2026 ; Published in Advance April 8, 2026, doi:10.1101/gr.280985.125
    ...within cellular genomics, we find significant advances in single-cell analysis. For instance, single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomics by offering advantages over traditional bulk analysis (Stegle et al. 2015; Bacher and Kendziorski 2016). Single-cell transcriptomics has...
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  3. Method: High-fidelity bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND
    • Kehan Lang,
    • Chenyang Jia,
    • Siyu Li,
    • Yi Guo,
    • Dingjun Hu,
    • and Shengquan Chen
    Genome Res. April 2026 36: 769-784; Published in Advance March 26, 2026, doi:10.1101/gr.281350.125
    ...of one modality from another. However, existing methods for cross-modality translation between single-cell RNA sequencing (scRNA-seq) and single-cell DNA methylation (scDNAm) data face limitations, including unidirectionality, inadequate modeling of context-specific DNA methylation...
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  4. Method: Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene coexpression networks
    • Prashanthi Ravichandran,
    • Princy Parsana,
    • Rebecca Keener,
    • Kasper D. Hansen,
    • and Alexis Battle
    Genome Res. September 2025 35: 2087-2103; Published in Advance July 17, 2025, doi:10.1101/gr.280808.125
    ...and AI Institute, Johns Hopkins University, Baltimore, Maryland 21218, USA Corresponding author: ajbattle@jhu.eduAbstractGene coexpression networks (GCNs) describe relationships among genes that maintain cellular identity and homeostasis. However, typical RNA-seq experiments often lack sufficient sample...
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  5. 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
    ...of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh; 4Department of Integrative Physiology, Baylor College of Medicine, Houston, Texas 77030, USA Corresponding authors: msrahman@cse.buet.ac.bd, samee@bcm.eduAbstractThe surge in single-cell data sets...
  6. Resource: Multitissue single-nucleus RNA-seq reveals cell type–specific regulatory patterns of alternative polyadenylation in pigs
    • Qiuhan Wen,
    • Zhen Wang,
    • Qi Bao,
    • Tianli Ding,
    • Haihan Zhang,
    • Jianbo Li,
    • Zhuang Liu,
    • Jieping Huang,
    • and Guoqiang Yi
    Genome Res. September 2025 35: 2116-2129; Published in Advance June 16, 2025, doi:10.1101/gr.280095.124
    ...polyadenylation (APA) plays a crucial role in gene regulation and phenotypic diversity. Whereas extensive studies have explored the global APA landscape using bulk RNA-seq data, in-depth analyses of APA events at the single-cell level remain limited—particularly in farm animals. In this study, we construct...
  7. 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
    ...is robust against spatial data noiseTo validate the noise tolerance of Polyomino, we simulated ground-truth ST data by substituting each spatial cell of seqFISH+ data by the single cell from snRNA-seq data of the same tissue (Fig. 2E; Tasic et al. 2018). Stringent comparison was performed between Polyomino...
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  8. Method: Tree-based differential testing using inferential uncertainty for RNA-seq
    • Noor Pratap Singh,
    • Euphy Y. Wu,
    • Jason Fan,
    • Michael I. Love,
    • and Rob Patro
    Genome Res. October 2025 35: 2326-2338; Published in Advance August 21, 2025, doi:10.1101/gr.279981.124
    ...also be explored in the future.Finally, it would be intriguing to explore the development of a TreeTerminus- and mehenDi-like approach in the context of single-cell RNA-seq analysis, where there are quite distinct but related opportunities for data-driven aggregation to increase the power...
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  9. 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
    ...cellular contexts in single-cell RNA sequencing (scRNA-seq) data. Based on the topology of these functional networks, DeCEP identifies context-dependent hub genes, calculates DeCEP scores, and determines the states of individual cells. (B) DeCEP anchors cell states to spatial locations in spatial...
  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
    ...intestine, spleen, and thymus, and three different data modalities, which include single-cell RNA-seq (scRNA-seq), single-cell ATAC-seq (scATAC-seq), and spatial transcriptomics (Slide-seq). Figure 1, A through C, presents an overview of GIANT. We first construct gene graphs for cell clusters from each...
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