Searching journal content for articles similar to Chamberlin et al. 34 (2): 179.

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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: 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...
  3. Method: Single-nucleus CUT&RUN elucidates the function of intrinsic and genomics-driven epigenetic heterogeneity in head and neck cancer progression
    • Howard J. Womersley,
    • Daniel Muliaditan,
    • Ramanuj DasGupta,
    • and Lih Feng Cheow
    Genome Res. January 2025 35: 162-177; Published in Advance December 2, 2024, doi:10.1101/gr.279105.124
    ...contributed equally to this work. Corresponding authors: dasguptar@gis.a-star.edu.sg, bieclf@nus.edu.sgAbstractInterrogating regulatory epigenetic alterations during tumor progression at the resolution of single cells has remained an understudied area of research. Here we developed a highly sensitive single-nucleus...
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  4. 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
    ...transcripts in a set of cells. By combining standard Illumina-based single-cell RNA-seq quantification of cultured cells’ transcriptomes with PacBio sequencing of the viral transcripts, it was found that this viral genetic variation explained part, but not all, of the heterogeneity in viral gene expression...
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  5. Method: Alignment of single-cell RNA-seq samples without overcorrection using kernel density matching
    • Mengjie Chen,
    • Qi Zhan,
    • Zepeng Mu,
    • Lili Wang,
    • Zhaohui Zheng,
    • Jinlin Miao,
    • Ping Zhu,
    • and Yang I. Li
    Genome Res. April 2021 31: 698-712; Published in Advance March 19, 2021, doi:10.1101/gr.261115.120
    ...with modest differences in cell type compositions, nearly all existing methods also removed true biological variation between cells from the different biopsy sites.Alignment improves power of eQTL mapping from population-level single-cell RNA-seq dataPCA is often used to estimate and correct batch effects...
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  6. Method: Analysis of alternative polyadenylation from single-cell RNA-seq using scDaPars reveals cell subpopulations invisible to gene expression
    • Yipeng Gao,
    • Lei Li,
    • Christopher I. Amos,
    • and Wei Li
    Genome Res. October 2021 31: 1856-1866; Published in Advance May 25, 2021, doi:10.1101/gr.271346.120
    ...proliferation and differentiation, but the APA heterogeneity among single cells remains largely unknown. Single-cell RNA sequencing (scRNA-seq) has been extensively used to define cell subpopulations at the transcription level. Yet, most scRNA-seq data have not been analyzed in an “APA-aware” manner. Here, we...
  7. Method: A deconvolution framework that uses single-cell sequencing plus a small benchmark data set for accurate analysis of cell type ratios in complex tissue samples
    • Shuai Guo,
    • Xiaoqian Liu,
    • Xuesen Cheng,
    • Yujie Jiang,
    • Shuangxi Ji,
    • Qingnan Liang,
    • Andrew Koval,
    • Yumei Li,
    • Leah A. Owen,
    • Ivana K. Kim,
    • Ana Aparicio,
    • Sanghoon Lee,
    • Anil K. Sood,
    • Scott Kopetz,
    • John Paul Shen,
    • John N. Weinstein,
    • Margaret M. DeAngelis,
    • Rui Chen,
    • and Wenyi Wang
    Genome Res. January 2025 35: 147-161; Published in Advance November 25, 2024, doi:10.1101/gr.278822.123
    ...retinal samples from donors’ eyes collected within 6-h postmortem (ages of death between 53 and 91) (Supplemental Table S1), for two batches of sequencing experiments. Both bulk and snRNA-seq profiling was performed on each sample from the same single-nucleus suspension aliquot using a template...
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  8. Method: Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE
    • Yingxin Lin,
    • Tung-Yu Wu,
    • Xi Chen,
    • Sheng Wan,
    • Brian Chao,
    • Jingxue Xin,
    • Jean Y.H. Yang,
    • Wing H. Wong,
    • and Y.X. Rachel Wang
    Genome Res. January 2024 34: 119-133; Published in Advance December 15, 2023, doi:10.1101/gr.277960.123
    .... Corresponding authors: rachel.wang@sydney.edu.au, whwong@stanford.eduAbstractSingle-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and sc...
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  9. Research: Single-cell spatial transcriptomics reveals a dystrophic trajectory following a developmental bifurcation of myoblast cell fates in facioscapulohumeral muscular dystrophy
    • Lujia Chen,
    • Xiangduo Kong,
    • Kevin G. Johnston,
    • Ali Mortazavi,
    • Todd C. Holmes,
    • Zhiqun Tan,
    • Kyoko Yokomori,
    • and Xiangmin Xu
    Genome Res. May 2024 34: 665-679; Published in Advance May 22, 2024, doi:10.1101/gr.278717.123
    ...correlation between the clinical phenotype and DUX4 target expression (Wang et al. 2019; Wong et al. 2024). Recent single-cell (sc) and single-nucleus (sn) RNA-seq studies have begun to address these questions (van den Heuvel et al. 2019; Jiang et al. 2020; Zheng et al. 2024). Our previous snRNA-seq analyses...
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  10. Method: A transcriptome-based single-cell biological age model and resource for tissue-specific aging measures
    • Shulin Mao,
    • Jiayu Su,
    • Longteng Wang,
    • Xiaochen Bo,
    • Cheng Li,
    • and Hebing Chen
    Genome Res. August 2023 33: 1381-1394; Published in Advance July 31, 2023, doi:10.1101/gr.277491.122
    ...the features learned from single-cell omics data can capture other dimensions of biological aging.Here, we developed single-cell aging-level estimator (SCALE), a tissue-specific measure that quantifies aging in individual cells using single-cell RNA-sequencing (scRNA-seq) data (Fig. 1). Unlike other clocks...
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