Searching journal content for articles similar to Li et al. 35 (10): 2300.

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Research: Oxygen-induced stress reveals context-specific gene regulatory effects in human brain organoids
- Benjamin D. Umans and
- Yoav Gilad
Genome Res. August 2025 35: 1689-1700; Published in Advance June 2, 2025, doi:10.1101/gr.280219.124
...NovaSeq 6000 instrument at the University of Chicago Genomics Core Facility (RRID:SCR_019196).Differential expression analysisTo identify genes differentially expressed between treatment conditions, we relied on well-established methods for analyzing bulk RNA sequencing data. Single-cell transcriptomes...
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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: 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...
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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
.... 2021). Even though high-resolution spatial transcriptome technologies have been developed, such technologies show significantly lower cell segmentation resolution and the number of detected genes compared with single-cell transcriptome data. Therefore, integrating single-cell and spatial transcriptomic...
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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
.... This makes them insensitive in complex environments where the detailed dynamics of cell interactions matter. We introduce CellAgentChat, an agent-based model (ABM) designed to decipher CCIs from single-cell RNA sequencing and spatial transcriptomics data. This approach models biological systems...
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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
...development, with each technology having varying spatial resolution (subcellular, single-cell, or multicellular regions), gene coverage (targeted vs. whole-transcriptome), and sequencing depth per location. For example, the widely used 10x Genomics Visium platform measures whole transcriptomes from multiple...
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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
...sequencing (scRNA-seq) and single-cell DNA methylation (scDNAm) data face limitations, including unidirectionality, inadequate modeling of context-specific DNA methylation–expression associations, neglect of biological relevance in evaluation, and poor performance in limited paired training data. To fill...
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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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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
...or repressed in different cell types, what cis-regulatory elements are linked to gene expression, and which key TFs regulate gene expression programs through these cis-elements.Employing the paired single-cell transcriptome and chromatin accessibility profiling method, we consistently identified major cell...
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Method: Unified integration of spatial transcriptomics across platforms with LLOKI
- Ellie Haber,
- Ajinkya Deshpande,
- Jian Ma,
- and Spencer Krieger
Genome Res. December 2025 35: 2722-2733; Published in Advance November 13, 2025, doi:10.1101/gr.280803.125
...(CZI Cell Science Program et al. 2025), and the Human Cell Atlas (Regev et al. 2017). Similarly, integrating spatial transcriptomics (ST) data sets, which contain both spatial coordinates and gene expression, enables comparative analysis across samples, technologies, and conditions, revealing cellular...
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