Searching journal content for articles similar to Li et al. 35 (6): 1429.

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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
    ....scPSS identifies damaged cells and damage progression in mouse infarcted heart tissueWe validated scPSS using single-cell transcriptomic data from mouse hearts before and after myocardial infarction (MI) (Calcagno et al. 2022). The data set contains labeled cardiomyocytes (CMs) from three distinct regions...
  2. 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
    ...) introduces the concept of cis-regulatory potential to infer subpopulation-specific GRNs, achieving significantly higher accuracy compared with correlation-based methods. CellOracle (Kamimoto et al. 2023) enables GRN inference by integrating single-cell multi-omics data with prior regulatory knowledge...
  3. 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
    ...FISH+ and matched single-cell data, shared cell types are identified. For each spatial cell, the most similar single cell (based on Pearson's correlation of gene expression) is selected and used as a substitute to simulate realistic mapping conditions. (F) Illustration of segmentation error. The left panel shows...
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  4. Resource: Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise
    • Aliza B. Rubenstein,
    • Gregory R. Smith,
    • Zidong Zhang,
    • Xi Chen,
    • Toby L. Chambers,
    • Frederique Ruf-Zamojski,
    • Natalia Mendelev,
    • Wan Sze Cheng,
    • Michel Zamojski,
    • Mary Anne S. Amper,
    • Venugopalan D. Nair,
    • Andrew R. Marderstein,
    • Stephen B. Montgomery,
    • Olga G. Troyanskaya,
    • Elena Zaslavsky,
    • Todd Trappe,
    • Scott Trappe,
    • and Stuart C. Sealfon
    Genome Res. July 2025 35: 1664-1677; Published in Advance May 20, 2025, doi:10.1101/gr.280051.124
    ...)-expressing fibro-adipogenic progenitor cells. Single-cell regulatory circuit triad reconstruction (transcription factor, chromatin interaction site, regulated gene) also identifies largely distinct gene regulatory circuits modulated by exercise in the three muscle fiber types and LUM-expressing fibro...
  5. 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...
  6. 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
    ...with 10 different tissues, three different major modalities, and nine different single-cell platforms. As we have shown, despite the considerable heterogeneity of the input data, our method was able to successfully group genes based on their function and pathways. We further used the method to assign new...
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  7. 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
    ..., highlighting its potential for broader applications in single-cell multiomics data integration.DiscussionIn this paper, scSHEFT is a label transfer method based on dual alignment from well-annotated scRNA-seq data to scATAC-seq data. scSHEFT takes gene expression counts, peak counts, and GAS as complementary...
  8. 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
    ...within conserved spatial structures. Moreover, when applied to the IFNB single-cell data set, where interferon beta stimulation induces a systematic shift in transcriptional profiles that can bias downstream analyses, our generalized geneCover framework is able to identify marker genes whose expressions...
  9. 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
    ...is systematically reduced for high signal genes (Supplemental Figs. 15–18).Nearest neighbor rank—embeddingsThe k-NN graph is a fundamental object that is used as input in several widely used algorithms in single-cell RNA-seq data analysis, for example, graph-based clustering (Leiden) and the creation of UMAP...
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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
    ...regeneration model. By employing chromium single-cell multiome technology from 10x Genomics, we captured the transcriptomic and epigenomic dynamics simultaneously in the same cells at single-cell resolution of regenerating fins at 1, 2, 4, and 6 days postamputation (dpa). Specifically, we identified both...
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