Searching journal content for articles similar to Guo et al. 35 (1): 147.

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  1. 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
    ...:Simulation-based evaluation of spatial reconstruction accuracyTo benchmark Polyomino against deconvolution-based spatial mapping methods, we constructed a simulation framework based on the STARMAP data set, which provides single-cell-resolution spatial transcriptomic data from the mouse brain. Spatial locations and gene...
    OPEN ACCESS ARTICLE
  2. Method: Highly accurate reference and method selection for universal cross–data set cell type annotation with CAMUS
    • Qunlun Shen,
    • Shuqin Zhang,
    • and Shihua Zhang
    Genome Res. November 2025 35: 2527-2538; Published in Advance October 1, 2025, doi:10.1101/gr.280821.125
    ...: (pancreas, mouse), Smart-seq2 (https://figshare.com/articles/dataset/Single-cell_RNA-seq_data_from_Smart-seq2_sequencing_of_FACS_sorted_cells_v2_/5829687/7). Data sets 5–7: (testis, human, monkey, mouse), Drop-seq, GSE142585 in the GEO database. Data set 8: (brain, human), snDrop-seq, GSE97942 in the GEO...
  3. 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
    ...-adipogenic progenitor cells, involving a total of 328 transcription factors acting at chromatin sites regulating 2025 genes. This web-accessible single-cell data set and regulatory circuitry map serve as a resource for understanding the molecular underpinnings of the metabolic and physiological effects of exercise...
  4. Method: A Bayesian framework to study tumor subclone–specific expression by combining bulk DNA and single-cell RNA sequencing data
    • Yi Qiao,
    • Xiaomeng Huang,
    • Philip J. Moos,
    • Jonathan M. Ahmann,
    • Anthony D. Pomicter,
    • Michael W. Deininger,
    • John C. Byrd,
    • Jennifer A. Woyach,
    • Deborah M. Stephens,
    • and Gabor T. Marth
    Genome Res. January 2024 34: 94-105; Published in Advance January 9, 2024, doi:10.1101/gr.278234.123
    ...cancer genomic studies with typical data sets. Simulation results indicated that our method is highly accurate with respect to a wide range of single-cell RNA sequencing true / false positive rates and subclone reconstruction error rates, and agnostic to subclone structure patterns. The breast cancer...
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  5. Method: spRefine denoises and imputes spatial transcriptomics with a reference-free framework powered by genomic language model
    • Tianyu Liu,
    • Tinglin Huang,
    • Wengong Jin,
    • Tinyi Chu,
    • Rex Ying,
    • and Hongyu Zhao
    Genome Res. February 3, 2026 gr.281001.125; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
    ...and missing19 gene measurements, challenges that are further compounded by the higher cost20 of spatial data compared to traditional single-cell data. To overcome this chal-21 lenge, we introduce spRefine, a deep learning framework that leverages genomic22 language models to jointly denoise and impute spatial...
    OPEN ACCESS ARTICLEACCEPTED MANUSCRIPT
  6. 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 sc...
  7. Perspective: Tagmentation-based single-cell genomics
    • Andrew C. Adey
    Genome Res. October 2021 31: 1693-1705; doi:10.1101/gr.275223.121
    ...-C, there is no enrichment for these ligation junctions, which would be the case for typical Hi-C technologies. Once the processed DNA for a single cell has been produced, tagmentation is performed with Tn5 that has been loaded with a set of 20 different adapter sequences, thus making the probability of producing a fragment...
  8. Method: Likelihood-based deconvolution of bulk gene expression data using single-cell references
    • Dan D. Erdmann-Pham,
    • Jonathan Fischer,
    • Justin Hong,
    • and Yun S. Song
    Genome Res. October 2021 31: 1794-1806; Published in Advance July 22, 2021, doi:10.1101/gr.272344.120
    ...) bulk data samples. Because neutrophils are notably difficult to assay accurately at the single-cell level, they were not present in either of the original reference panels. However, given the large fractions of neutrophils estimated by flow cytometry, particularly for the Newman et al. data set, we...
  9. Method: bigSCale: an analytical framework for big-scale single-cell data
    • Giovanni Iacono,
    • Elisabetta Mereu,
    • Amy Guillaumet-Adkins,
    • Roser Corominas,
    • Ivon Cuscó,
    • Gustavo Rodríguez-Esteban,
    • Marta Gut,
    • Luis Alberto Pérez-Jurado,
    • Ivo Gut,
    • and Holger Heyn
    Genome Res. June 2018 28: 878-890; Published in Advance May 3, 2018, doi:10.1101/gr.230771.117
    ...and sensitivitymake it suitable to address future challenges of large single-cell data sets. Results The bigSCale framework Data sets from scRNA-seq display sparse and noisy gene expression values, among other sources due to drop-out events, amplification biases, and variable sequencing depth. The bigSCale framework...
    OPEN ACCESS ARTICLE
  10. Method: Bayesian estimation of cell type–specific gene expression with prior derived from single-cell data
    • Jiebiao Wang,
    • Kathryn Roeder,
    • and Bernie Devlin
    Genome Res. October 2021 31: 1807-1818; Published in Advance April 9, 2021, doi:10.1101/gr.268722.120
    ...RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology. With current technologies, scRNA-seq data are known...
    OPEN ACCESS ARTICLE
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