Searching journal content for articles similar to Hsiao et al. 30 (4): 611.

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  1. 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
    ...cells. Although these methods can assign pathological scores at the single-cell level, they require labeled training data from both healthy and diseased individuals, limiting their applicability to well-characterized conditions.To address the need for a computational method for quantifying...
  2. 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. March 26, 2026 ; Published in Advance March 26, 2026, doi:10.1101/gr.281350.125
    ...sequencing (scRNA-seq) and single-cell DNA methylation (scDNAm) data face limitations, including unidirectionality, inadequate modeling of context-specific DNA methylation–expression associations, neglect of biological relevance in evaluation, and poor performance in limited paired training data. To fill...
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  3. 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
    ...sizes for reliable GCN inference. recount3, a data set with 316,443 processed human RNA-seq samples, provides an opportunity to improve network reconstruction. However, GCN inference from public data is challenged by confounders and inconsistent labeling. To address this, we develop a pipeline...
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  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. 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...
  6. 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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  7. 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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  8. 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
    ...combinations. By incorporating Tikhonov regularization, TFcomb guarantees the generalization ability of solutions and effectively focuses on key TFs that truly drive state transitions. TFcomb utilizes GRNs inferred from single-cell RNA-seq and ATAC-seq data, ensuring the inclusion of causal regulatory...
  9. Method: Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network
    • Xingyan Liu,
    • Qunlun Shen,
    • and Shihua Zhang
    Genome Res. January 2023 33: 96-111; Published in Advance December 16, 2022, doi:10.1101/gr.276868.122
    ...from scRNA-seq data.Single-cell RNA sequencing (scRNA-seq) has rapidly emerged as a powerful tool to characterize a large number of single-cell transcriptomes in different tissues, organs, and species (Kolodziejczyk et al. 2015). It not only deepens our knowledge of cells but also provides novel...
  10. Method: Harnessing agent-based frameworks in CellAgentChat to unravel cell–cell interactions from single-cell and spatial transcriptomics
    • Vishvak Raghavan,
    • Yumin Zheng,
    • Yue Li,
    • and Jun Ding
    Genome Res. July 2025 35: 1646-1663; Published in Advance May 2, 2025, doi:10.1101/gr.279771.124
    ..., focusing on cell population levels and losing single-cell resolution. Scriabin (Wilk et al. 2024) analyzes LR interactions at the single-cell level but primarily relies on unimodal scRNA-seq data, limiting CCI inference ability owing to lack of spatial information. Because CCIs in tissue environments...
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