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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: 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...
  3. 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
    ...as a fundamental technique for characterizing the functional state of individual cells. Complementary to scRNAseq, single-cell DNA methylation (scDNAm) sequencing provides -wide maps of epigenetic modifications at single-cell resolution, offering insights into the regulatory mechanisms underlying gene expression...
    OPEN ACCESS ARTICLE
  4. 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...
  5. 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
    ...-staged/healthy ones in PD brains, we first applied the deep learning model to the PD single-cell transcriptome data using patient information (disease diagnosis, age, and sex) as binary training data label (0 or 1; see Methods) (Supplemental Fig. S1G). Although the deep learning model separated cells of PD patients...
    OPEN ACCESS ARTICLE
  6. 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
  7. 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
    ...:Simulation-based evaluation of spatial reconstruction accuracyTo benchmark Polyomino against deconvolution-based spatial mapping methods, we constructed a simulation framework based on the STARMAP data set, which provides single-cell-resolution spatial transcriptomic data from the mouse brain. Spatial locations and gene...
    OPEN ACCESS ARTICLE
  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
    ...Harnessing agent-based frameworks in CellAgentChat to unravel cell–cell interactions from single-cell and spatial transcriptomics Vishvak Raghavan1,2,3, Yumin Zheng2, Yue Li1,3 and Jun Ding1,2,3 1School of Computer Science, McGill University, Montreal, Quebec H3A 2A7, Canada; 2Meakins...
    OPEN ACCESS ARTICLE
  9. 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
    ...the ability of GIANT on integrating data from different modalities, we collected single-cell mouse brain data from the Allen Brain Cell Atlas (Yao et al. 2023), which contains 2,341,350 cells from scRNA-seq and 3,938,808 cells from MERFISH spatial transcriptomics (Chen et al. 2015). Both modalities were...
    OPEN ACCESS ARTICLE
  10. 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...
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