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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
    ...the significance of cellular state deviations from a healthy reference, we introduce single-cell Pathological Shift Scoring (scPSS). scPSS uses gene expression profiles from normal cells to establish a reference state distribution, using k-nearest neighbor distances in principal component embedding space. For any...
  2. 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...
  3. 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
    ...biologically meaningful structures in spatial transcriptomics data. Its performance underscores its potential for providing more accurate and biologically relevant insights into additional spatial transcriptomic and scRNA-seq data.geneCover improves resolution in single-cell and spatial transcriptomics...
  4. 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
    ...Interdisciplinary Studies, Nankai University, Tianjin 300071, China Single-cell multiomic sequencing technologies have offered unprecedented insights into cellular heterogeneity by jointly profiling gene expression and epigenetic landscapes at single-cell resolution. However, the application of these technologies...
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  5. Method: spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model
    • Tianyu Liu,
    • Tinglin Huang,
    • Wengong Jin,
    • Tinyi Chu,
    • Rex Ying,
    • and Hongyu Zhao
    Genome Res. February 3, 2026 ; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
    ...that are further compounded by the higher cost of spatial data compared to traditional single-cell data. To overcome this challenge, we introduce spRefine, a deep learning framework that leverages genomic language models to jointly denoise and impute spatial transcriptomic data. Our results demonstrate that sp...
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  6. 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
    ...and multi-cell/spot resolutionsTo demonstrate CellAgentChat's accuracy in predicting CCIs, we applied it to single-cell spatial transcriptomics data from the Stereo-seq platform during mouse hippocampus development (Chen et al. 2022). We focused on identifying key signaling pathways involved...
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  7. 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...
  8. 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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  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
    ...for the development of computational methods to extract deeper insights and further our understanding of biological systems. Rapid innovation in the long-read sequencing space has enabled full-length single-cell RNA isoform sequencing, pushing the boundaries of transcriptome research. This leap in resolution has...
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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...
    OPEN ACCESS ARTICLE
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