Searching journal content for articles similar to Li et al. 32 (9): 1765.

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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
    ...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...
  2. 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
    ...for Imaging Science, Johns Hopkins University, Baltimore, Maryland 21218, USA Corresponding author: awang87@jhu.eduAbstractThe selection of marker gene panels is critical for capturing the cellular and spatial heterogeneity in the expanding atlases of single-cell RNA sequencing (scRNA-seq) and spatial...
  3. 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
    .... Recent advancements in single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) technologies have empowered the comprehensive characterization of gene programs at both single-cell and spatial resolutions. Here, we present DeCEP, a computational framework designed to characterize context...
  4. 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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  5. Method: Batch correction methods used in single-cell RNA sequencing analyses are often poorly calibrated
    • Sindri Emmanúel Antonsson and
    • Páll Melsted
    Genome Res. August 2025 35: 1832-1841; Published in Advance July 7, 2025, doi:10.1101/gr.279886.124
    ...performed better than this baseline and altered the data less than downsampling. Seurat performed better than downsampling on the heart data. Detailed cluster similarity measures can be found in Supplemental Tables 1–4.Differential expressionA common part of the single-cell RNA-seq workflow is finding those...
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  6. 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
    ....DiscussionIn this study, we systematically evaluated scRNA-seq gene signature scoring methods for unsupervised cell annotation, an approach widely used in single-cell studies to characterize cellular populations. In the context of cancer research, in which transcriptional programs and diverse cellular states are strongly...
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  7. 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
    ...equally to this work. Corresponding author: zsh@amss.ac.cnAbstractCross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow us to explore, at single-cell resolution, the origins of the cellular diversity and evolutionary mechanisms that shape cellular form and function. Cell...
  8. Research: Long-read single-cell RNA sequencing enables the study of cancer subclone-specific genotypes and phenotypes in chronic lymphocytic leukemia
    • Gage S. Black,
    • Xiaomeng Huang,
    • Yi Qiao,
    • Philip Moos,
    • Deepa Sampath,
    • Deborah M. Stephens,
    • Jennifer A. Woyach,
    • and Gabor T. Marth
    Genome Res. April 2025 35: 686-697; Published in Advance February 18, 2025, doi:10.1101/gr.279049.124
    ...-mutated subclones exhibit distinct transcriptomic behavior when compared to other cancer subclones. To achieve these goals, we use scBayes, which integrates bulk DNA sequencing and single-cell RNA sequencing (scRNA-seq) data to genotype individual cells for subclone-defining mutations. Although the most common...
  9. Method: Accurate fusion transcript identification from long- and short-read isoform sequencing at bulk or single-cell resolution
    • Qian Qin,
    • Victoria Popic,
    • Kirsty Wienand,
    • Houlin Yu,
    • Emily White,
    • Akanksha Khorgade,
    • Asa Shin,
    • Christophe Georgescu,
    • Catarina D. Campbell,
    • Arthur Dondi,
    • Niko Beerenwinkel,
    • Francisca Vazquez,
    • Aziz M. Al'Khafaji,
    • and Brian J. Haas
    Genome Res. April 2025 35: 967-986; Published in Advance March 14, 2025, doi:10.1101/gr.279200.124
    ...short-read sequences. Recent advances in long-read isoform sequencing enable the detection of fusion transcripts at unprecedented resolution in bulk and single-cell samples. Here, we developed a new computational tool, CTAT-LR-Fusion, to detect fusion transcripts from long-read RNA-seq with or without...
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  10. Resource: Common and specific gene regulatory programs in zebrafish caudal fin regeneration at single-cell resolution
    • Yujie Chen,
    • Yiran Hou,
    • Qinglin Zeng,
    • Irene Wang,
    • Meiru Shang,
    • Kwangdeok Shin,
    • Christopher Hemauer,
    • Xiaoyun Xing,
    • Junsu Kang,
    • Guoyan Zhao,
    • and Ting Wang
    Genome Res. January 2025 35: 202-218; Published in Advance January 14, 2025, doi:10.1101/gr.279372.124
    ..., prrx1b, cdh11) (Fig. 1B,C; Supplemental Fig. S1A; Supplemental Table S1B). Compared with our previous single-cell RNA-seq study (Hou et al. 2020), this work greatly increased the number of cells assayed (83,282 vs. 18,388). As a result, we were able to identify additional cell types, including pigment...
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