Searching journal content for articles similar to Gupta et al. 31 (4): 689.

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  1. Method: scSHEFT enables multiomics label transfer from scRNA-seq to scATAC-seq through dual alignment
    • Zhitao Huang,
    • Ruiqing Zheng,
    • Pengzhen Jia,
    • Xuhua Yan,
    • Jinmiao Chen,
    • and Min Li
    Genome Res. February 2026 36: 387-396; Published in Advance January 20, 2026, doi:10.1101/gr.280410.125
    ...can be divided into two types. Their major difference lies in the way to process the raw omics features. The first type of methods directly uses gene expression count data from scRNA-seq and peak count data from scATAC-seq as input to the model, and uses omics-specific neural networks to embed them...
  2. Method: Diffusion-based generation of gene regulatory networks from scRNA-seq data with DigNet
    • Chuanyuan Wang and
    • Zhi-Ping Liu
    Genome Res. February 2025 35: 340-354; Published in Advance December 18, 2024, doi:10.1101/gr.279551.124
    ...and genetic contexts. Here, we propose a discrete diffusion generation model, called DigNet, capable of generating corresponding GRNs from high-throughput single-cell RNA sequencing (scRNA-seq) data. DigNet embeds the network generation process into a multistep recovery procedure with Markov properties. Each...
    OPEN ACCESS ARTICLE
  3. Method: Benchmarking bulk and single-cell variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries
    • Matthew Wiens,
    • Hossein Farahani,
    • R. Wilder Scott,
    • T. Michael Underhill,
    • and Ali Bashashati
    Genome Res. August 2024 34: 1196-1210; Published in Advance August 15, 2024, doi:10.1101/gr.277066.122
    ...such as scRNA-seq and scATAC-seq have become widespread and effective tools to interrogate tissue composition. Increasingly, variant callers are being applied to these methodologies to resolve the genetic heterogeneity of a sample, especially in the case of detecting the clonal architecture of a tumor...
  4. Method: Systematic mapping of TF-mediated cell fate changes by a pooled induction coupled with scRNA-seq and multi-omics approaches
    • Muyoung Lee,
    • Qingqing Guo,
    • Mijeong Kim,
    • Joonhyuk Choi,
    • Alia Segura,
    • Alper Genceroglu,
    • Lucy LeBlanc,
    • Nereida Ramirez,
    • Yu Jin Jang,
    • Yeejin Jang,
    • Bum-Kyu Lee,
    • Edward M. Marcotte,
    • and Jonghwan Kim
    Genome Res. March 2024 34: 484-497; Published in Advance April 5, 2024, doi:10.1101/gr.277926.123
    ...of TF induction (Parekh et al. 2018; Joung et al. 2023). Furthermore, none of the studies investigated the dynamic changes in the transcriptome or the action mechanisms of potent TFs during cell fate conversion.The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding...
    OPEN ACCESS ARTICLE
  5. Method: Debiased personalized gene coexpression networks for population-scale scRNA-seq data
    • Shan Lu and
    • Sunduz Keles
    Genome Res. June 9, 2023 gr.277363.122; Published in Advance June 9, 2023, doi:10.1101/gr.277363.122
    ...to be severely biased towards zero for genes with low and sparse expression. Here, we present Dozer to debias gene-gene correlation estimates from scRNA-seq datasets and accurately quantify network level variation across individuals. Dozer corrects correlation estimates in the general Poisson measurement model...
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  6. Method: Recalibrating differential gene expression by genetic dosage variance prioritizes functionally relevant genes
    • Philipp Rentzsch,
    • Aaron Kollotzek,
    • Kaushik Ram Ganapathy,
    • Pejman Mohammadi,
    • and Tuuli Lappalainen
    Genome Res. October 2025 35: 2316-2325; Published in Advance September 17, 2025, doi:10.1101/gr.280360.124
    ...on genetic expression variance observed in the human population. The newly established metric ranks statistically differentially expressed genes, not by nominal change of expression, but by relative change in comparison to natural dosage variation for each gene. We apply our method to RNA sequencing data...
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  7. Research: Distinct classes of lamina-associated domains are defined by differential patterns of repressive histone methylation
    • Caden J. Martin,
    • Elizabeth A. Oser,
    • Prabakaran Nagarajan,
    • Liudmila V. Popova,
    • Benjamin D. Sunkel,
    • Benjamin Z. Stanton,
    • and Mark R. Parthun
    Genome Res. September 2025 35: 1959-1974; Published in Advance August 5, 2025, doi:10.1101/gr.280380.124
    ...and regulation. One is the large degree of redundancy between tethering mechanisms and the diversity in peripheral proteins which can interact with chromatin, many of which are differentially expressed between different tissues and stages of development. Added to this is the variability of LADs themselves...
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  8. Method: Tree-based differential testing using inferential uncertainty for RNA-seq
    • Noor Pratap Singh,
    • Euphy Y. Wu,
    • Jason Fan,
    • Michael I. Love,
    • and Rob Patro
    Genome Res. October 2025 35: 2326-2338; Published in Advance August 21, 2025, doi:10.1101/gr.279981.124
    ...of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, USA Corresponding author: rob@cs.umd.eduAbstractIdentifying differentially expressed transcripts poses a crucial yet challenging problem in transcriptomics. Substantial uncertainty is associated with the abundance...
    OPEN ACCESS ARTICLE
  9. Method: Accurate estimation of pathway activity in single cells for clustering and differential analysis
    • Daniel Davis,
    • Avishai Wizel,
    • and Yotam Drier
    Genome Res. June 2024 34: 925-936; Published in Advance July 9, 2024, doi:10.1101/gr.278431.123
    ...on the normalized expression of all genes, weighted by their relative rank. Using SiPSiC, we reanalyzed scRNA-seq data of COVID-19, lung adenocarcinoma, and glioma; identified both known and novel cellular pathways involved in these diseases; and demonstrated SiPSiC's high accuracy and superior ability to identify...
    OPEN ACCESS ARTICLE
  10. Method: Modeling gene interactions in polygenic prediction via geometric deep learning
    • Han Li,
    • Jianyang Zeng,
    • Michael P. Snyder,
    • and Sai Zhang
    Genome Res. January 2025 35: 178-187; Published in Advance November 19, 2024, doi:10.1101/gr.279694.124
    ...-Net, an interpretable geometric deep learning–based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the -wide PRS at the single-gene resolution and then explicitly encapsulates gene–gene interactions...
    OPEN ACCESS ARTICLE
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