Searching journal content for articles similar to Elyanow et al. 30 (2): 195.

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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
...directed GRNs by manually distinguishing TFs from target genes. More recently, deep learning has been introduced into GRN inference, with methods such as CNNGRN, which leverages convolutional neural networks to analyze bulk time-series gene expression data and infer regulatory interactions between TFs...
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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: 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
..., manual annotation at single-cell resolution requires expert knowledge, making the process both time-consuming and resource-intensive.In contrast, most existing label-free marker gene selection methods, which do not rely on predefined cell type labels, adopt an imputation-based objective. These methods...
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Method: Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms
- Hongyu Zheng,
- Hirak Sarkar,
- and Benjamin J. Raphael
Genome Res. December 2025 35: 2734-2743; Published in Advance November 17, 2025, doi:10.1101/gr.280555.125
...2020) impute genes in a SRT data set by learning a mapping between each spatial location and the reference single cells. On the other hand, akin to the modeling of single-cell data sets, several recent methods use low-dimensional factorization (Chidester et al. 2023; Townes and Engelhardt 2023...
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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
...to generate animated visualizations of single-cell interactions and provides flexibility in modifying agent behavior rules, facilitating thorough exploration of both close and distant cellular communications. Furthermore, CellAgentChat leverages ABM features to enable intuitive in silico perturbations via...
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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
...'s capacity to compensate for low spatial transcriptomic coverage by effectively leveraging the resolution and completeness of single-cell data, accurately reconstructing gene expression in undersequenced spatial regions (Supplemental Fig. S6). These results highlight the exceptional efficiency...
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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
...principal component (PC) embeddings of gene expression are popular for single-cell analysis, with distances in PC space being used to cluster cells into groups (Fa et al. 2021) and measure differences between these groups (Nicol et al. 2024). Furthermore, contrastive methods (Abid et al. 2018; Gorla et al...
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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
..., highlighting the extensive gene interactions within organisms. Therefore, we propose scHGR, an automated annotation tool designed to leverage gene regulatory relationships in constructing gene-mediated cell communication graphs for single-cell transcriptome data. This strategy helps reduce noise from diverse...
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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
...)-expressing fibro-adipogenic progenitor cells. Single-cell regulatory circuit triad reconstruction (transcription factor, chromatin interaction site, regulated gene) also identifies largely distinct gene regulatory circuits modulated by exercise in the three muscle fiber types and LUM-expressing fibro...
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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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