Searching journal content for articles similar to Khan et al. 36 (2): 375.

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  1. 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
    .... Corresponding authors: chen_jiekai@gibh.ac.cn, lin_lihui@gibh.ac.cnAbstractIntegration of single-cell and spatial transcriptomes represents a fundamental strategy to enhance spatial data quality. However, existing methods for mapping single-cell data to spatial coordinates struggle with large-scale data sets...
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  2. Method: Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach
    • Fabien Wehbe,
    • Levi Adams,
    • Jordan Babadoudou,
    • Samantha Yuen,
    • Yoon-Seong Kim,
    • and Yoshiaki Tanaka
    Genome Res. January 2025 35: 135-146; Published in Advance December 2, 2024, doi:10.1101/gr.278812.123
    ...IDST), that infers the disease progression levels of individual cells in single-cell transcriptome profiles with weakly supervised deep learning. The weak supervision models utilize labeling functions that are automatically generated from a small subset of labeled data sets and give weak labels on large unclear data...
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  3. 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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  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
    ...). In particular, single-cell multi-omics technologies like SHARE-seq (Ma et al. 2020) can simultaneously profile transcriptomic and epigenomic data within individual cells, enabling the interrogation of cellular heterogeneity and molecular hierarchy (Cao et al. 2024). Consequently, numerous methods have emerged...
  5. 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
    ...signatures from rare sources of variability. To facilitate the discovery of genes associated with all sources of transcriptomic variability, we introduce geneCover, a label-free correlation-based marker gene selection method designed for single-cell RNA sequencing and spatial transcriptomics data. gene...
  6. 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
    ...; Jensen et al. 2020; Hokken et al. 2021; Egan and Sharples 2023). Insight into the coordinated transcriptomic and epigenomic molecular responses to exercise within each of the diverse muscle cell types is lacking; single-cell (sc) assays enable the characterization of the exercise response within...
  7. 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...
  8. 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
    ...concerning the single-cell data, typically set to two for spatial transcriptomics data that is derived from two-dimensional slices. δ signifies the decay rate of ligand diffusion, which prioritizes interactions over long or short distances. CellAgentChat quantifies interactions at multiple levels: between...
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  9. Review: Advancements in prospective single-cell lineage barcoding and their applications in research
    • Xiaoli Zhang,
    • Yirui Huang,
    • Yajing Yang,
    • Qi-En Wang,
    • and Lang Li
    Genome Res. December 2024 34: 2147-2162; Published in Advance November 21, 2024, doi:10.1101/gr.278944.124
    ...the prospective tracking of millions of individual cells simultaneously, providing a unique opportunity to trace cellular lineages over time (Wagner and Klein 2020). The integration of single-cell lineage tracing (scLT) and single-cell transcriptomics presents a significant opportunity to explore clonal...
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  10. Review: Understanding isoform expression by pairing long-read sequencing with single-cell and spatial transcriptomics
    • Natan Belchikov,
    • Justine Hsu,
    • Xiang Jennie Li,
    • Julien Jarroux,
    • Wen Hu,
    • Anoushka Joglekar,
    • and Hagen U. Tilgner
    Genome Res. November 2024 34: 1735-1746; doi:10.1101/gr.279640.124
    ..., rely on short reads and offer the ability to transcriptomically compare different cell types but are limited in their ability to measure differential isoform expression. More recently, long-read sequencing methods have been combined with single-cell and spatial technologies in order to characterize...
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