Searching journal content for articles similar to Chen et al. 34 (5): 665.

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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...
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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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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...
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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
...state transitions. To gain a more comprehensive understanding of cellular states and gene regulatory mechanisms, researchers have developed single-cell multiomic sequencing technologies (Demetci et al. 2022; Li et al. 2024b). These technologies allow the simultaneous profiling of transcriptomic...
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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...
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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...
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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
...-specific isoform diversity (Joglekar et al. 2021). Analysis of two forms of glioma revealed the role of isoform diversity in brain cancer, also utilizing the Visium platform (Ren et al. 2023). In both studies, spatial transcriptomics was supplemented by single-cell transcriptomics to provide a broader picture...
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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
..., which is characterized by several waves of gene upregulation and downregulation. We identified and in vivo validated cell-type-specific and position-specific regeneration-responsive enhancers and constructed regulatory networks by cell type and stage. Our single-cell resolution transcriptomic...
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Research: Developmental transcriptomics in Pristionchus reveals the environmental responsiveness of a plasticity gene-regulatory network
- Shelley Reich,
- Tobias Loschko,
- Julie Jung,
- Samantha Nestel,
- Ralf J. Sommer,
- and Michael S. Werner
Genome Res. July 2025 35: 1560-1573; Published in Advance June 9, 2025, doi:10.1101/gr.279783.124
...expectations for when differential gene expression should matter. Here, we collated existing data into a gene-regulatory network (GRN) and performed developmental transcriptomics across different environmental conditions, genetic backgrounds, and mutants to assess the regulatory logic of mouth-form plasticity...
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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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