Searching journal content for articles similar to Chen et al. 31 (4): 698.

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  1. Method: Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene coexpression networks
    • Prashanthi Ravichandran,
    • Princy Parsana,
    • Rebecca Keener,
    • Kasper D. Hansen,
    • and Alexis Battle
    Genome Res. September 2025 35: 2087-2103; Published in Advance July 17, 2025, doi:10.1101/gr.280808.125
    ...and AI Institute, Johns Hopkins University, Baltimore, Maryland 21218, USA Corresponding author: ajbattle@jhu.eduAbstractGene coexpression networks (GCNs) describe relationships among genes that maintain cellular identity and homeostasis. However, typical RNA-seq experiments often lack sufficient sample...
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  2. 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
    ...of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka-1000, Bangladesh; 4Department of Integrative Physiology, Baylor College of Medicine, Houston, Texas 77030, USA Corresponding authors: msrahman@cse.buet.ac.bd, samee@bcm.eduAbstractThe surge in single-cell data sets...
  3. 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...
  4. Method: Efficient integration of spatial omics data for joint domain detection, matching, and alignment with stMSA
    • Han Shu,
    • Jing Chen,
    • Chang Xu,
    • Jialu Hu,
    • Yongtian Wang,
    • Jiajie Peng,
    • Qinghua Jiang,
    • Xuequn Shang,
    • and Tao Wang
    Genome Res. October 2025 35: 2285-2299; Published in Advance August 14, 2025, doi:10.1101/gr.280584.125
    ...technologies. Furthermore, stMSA effectively deciphers complex developmental trajectories by integrating spatial proteomics and transcriptomics data and excels in cross-slice matching and alignment for 3D tissue reconstruction.Spatial omics (SO) technologies have greatly enhanced our understanding of molecular...
  5. Method: Accurate integration of multiple heterogeneous single-cell RNA-seq data sets by learning contrastive biological variation
    • Yang Zhou,
    • Qiongyu Sheng,
    • Jing Qi,
    • Jiao Hua,
    • Bo Yang,
    • Lei Wan,
    • and Shuilin Jin
    Genome Res. May 2023 33: 750-762; Published in Advance June 12, 2023, doi:10.1101/gr.277522.122
    ..., an integrative study of multiple heterogeneous single-cell RNA sequencing (scRNA-seq) data sets is crucial. However, present approaches are unable to integrate diverse data sets from various biological conditions effectively because of the confounding effects of biological and technical differences. We introduce...
  6. 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
    ...is robust against spatial data noiseTo validate the noise tolerance of Polyomino, we simulated ground-truth ST data by substituting each spatial cell of seqFISH+ data by the single cell from snRNA-seq data of the same tissue (Fig. 2E; Tasic et al. 2018). Stringent comparison was performed between Polyomino...
    OPEN ACCESS ARTICLE
  7. Method: Tree-based differential testing using inferential uncertainty for RNA-seq
    • Noor Pratap Singh,
    • Euphy Y. Wu,
    • Jason Fan,
    • Michael I. Love,
    • and Rob Patro
    Genome Res. October 2025 35: 2326-2338; Published in Advance August 21, 2025, doi:10.1101/gr.279981.124
    ...also be explored in the future.Finally, it would be intriguing to explore the development of a TreeTerminus- and mehenDi-like approach in the context of single-cell RNA-seq analysis, where there are quite distinct but related opportunities for data-driven aggregation to increase the power...
    OPEN ACCESS ARTICLE
  8. 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...
  9. RECOMB 2022 Special/Methods: Unsupervised cell functional annotation for single-cell RNA-seq
    • Dongshunyi Li,
    • Jun Ding,
    • and Ziv Bar-Joseph
    Genome Res. September 2022 32: 1765-1775; Published in Advance June 28, 2022, doi:10.1101/gr.276609.122
    ...-specific cis-eQTLs and co-expression QTLs. Nat Genet 50: 493–497. doi:10.1038/s41588-018-0089-9 ↵Wang B, Zhu J, Pierson E, Ramazzotti D, Batzoglou S. 2017. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat Methods 14: 414–416. doi:10.1038/nmeth.4207 ↵Wei Z...
  10. Method: A gene regulatory network–aware graph learning method for cell identity annotation in single-cell RNA-seq data
    • Mengyuan Zhao,
    • Jiawei Li,
    • Xiaoyi Liu,
    • Ke Ma,
    • Jijun Tang,
    • and Fei Guo
    Genome Res. July 2024 34: 1036-1051; Published in Advance August 12, 2024, doi:10.1101/gr.278439.123
    ....In single-cell transcriptome sequencing, high-resolution quantification of gene expression profiles provides insights into cellular heterogeneity and the molecular underpinnings of tissue phenotype variations (Kalucka et al. 2020; Argelaguet et al. 2021). Analyzing cell type composition using scRNA-seq...
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