Searching journal content for articles similar to Deng et al. 33 (10): 1690.

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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
    ...the significance of cellular state deviations from a healthy reference, we introduce single-cell Pathological Shift Scoring (scPSS). scPSS uses gene expression profiles from normal cells to establish a reference state distribution, using k-nearest neighbor distances in principal component embedding space. For any...
  2. 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
    ...expression profiles were obtained for individual cells after standard quality control and filtering.To mimic lower-resolution spatial transcriptomic platforms, we simulated 189 spatial pseudospots by aggregating transcriptomes of spatially adjacent single cells. For each pseudospot, the aggregated expression...
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  3. Research: Degrees of convergent evolution in rodent adaptations to arid environments
    • Domitille Chalopin,
    • Carine Rey,
    • Jeremy Ganofsky,
    • Juliana Blin,
    • Pascale Chevret,
    • Marion Mouginot,
    • Laurent Guéguen,
    • Bastien Boussau,
    • Sophie Pantalacci,
    • and Marie Sémon
    Genome Res. March 2026 36: 472-486; Published in Advance January 21, 2026, doi:10.1101/gr.280089.124
    ..._expr but not in the recent_expr data set (Supplemental Fig. S25; Supplemental Table S10). This was also coherent with dendrogram analyses of DE genes (Supplemental Fig. S26).Altogether, whole-transcriptome patterns, gene modules, and differential expressed genes detected by both methods show more convergent evolution...
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  4. 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...
  5. Research: Integrating short-read and long-read single-cell RNA sequencing for comprehensive transcriptome profiling in mouse retina
    • Meng Wang,
    • Yumei Li,
    • Jun Wang,
    • Soo Hwan Oh,
    • Yexuan Cao,
    • and Rui Chen
    Genome Res. April 2025 35: 740-754; Published in Advance March 6, 2025, doi:10.1101/gr.279167.124
    ...a systematic comparison between single-cell long-read and conventional short-read RNA sequencing techniques. The transcriptome of approximately 30,000 mouse retina cells was profiled using 1.54 billion Illumina short reads and 1.40 billion Oxford Nanopore Technologies long reads. Consequently, we identify 44...
  6. 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
    ...intestine, spleen, and thymus, and three different data modalities, which include single-cell RNA-seq (scRNA-seq), single-cell ATAC-seq (scATAC-seq), and spatial transcriptomics (Slide-seq). Figure 1, A through C, presents an overview of GIANT. We first construct gene graphs for cell clusters from each...
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  7. 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...
  8. 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
    ...; Jensen et al. 2020; Hokken et al. 2021; Egan and Sharples 2023). Insight into the coordinated transcriptomic and epigenomic molecular responses to exercise within each of the diverse muscle cell types is lacking; single-cell (sc) assays enable the characterization of the exercise response within...
  9. Research: Autism spectrum disorder risk genes have convergent effects on transcription and neuronal firing patterns in primary neurons
    • Alekh Paranjapye,
    • Rili Ahmad,
    • Steven Su,
    • Abraham J. Waldman,
    • Jennifer E. Phillips-Cremins,
    • Shuo Zhang,
    • and Erica Korb
    Genome Res. November 2025 35: 2433-2444; Published in Advance October 20, 2025, doi:10.1101/gr.280698.125
    ...changes in firing patterns with decreased spike number over multiple developmental time points (Fig. 5B; Supplemental Fig. S6C). Notably, although control neurons show anticipated increases in spiking with maturity, depletion of nearly every transcriptional regulator dampened or fully blocked...
  10. 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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