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<p>Functional perturbations of genes do not always cause expression changes, but can manifest through network rewiring or context-specific shifts in regulatory activity. However, inferring functional shifts in genes and linking them to the specific cell populations remains challenging, as most current scRNA-seq data analysis focuses either on differential gene expression or on cell abundance/state changes, but rarely associate gene perturbations with particular cell populations. Here we present scDNS, a framework that quantifies gene-specific functional perturbations by measuring information-theoretic divergence between condition-specific gene interaction network configurations. In simulated stress tests and multiple experimental datasets, scDNS prioritizes key regulators and perturbed cell populations,&#xa0;even when expression changes are minimal but network rewiring is pronounced. Applications to immunodeficiency mutations, stimulus responses, and viral infection reveal hidden regulatory programs and heterogeneous responder cell states. In pancreatic cancer, scDNS nominates TIMM44 as a mitochondrial sensitizer enhancing gemcitabine efficacy. Together, scDNS provides a powerful tool for inferring dynamic gene perturbations in single cells.</p>

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Characterizing gene perturbations in single cells via network divergence analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月11日

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  • Chao Huang,
  • Yuhan Li,
  • Botao Fa,
  • Jinglin Zhu,
  • Zhenni Liu,
  • Yixin Ma,
  • Zhitao Zhang,
  • Yungang Xu,
  • Qiuran Xu,
  • Zhengtao Xiao

摘要

Functional perturbations of genes do not always cause expression changes, but can manifest through network rewiring or context-specific shifts in regulatory activity. However, inferring functional shifts in genes and linking them to the specific cell populations remains challenging, as most current scRNA-seq data analysis focuses either on differential gene expression or on cell abundance/state changes, but rarely associate gene perturbations with particular cell populations. Here we present scDNS, a framework that quantifies gene-specific functional perturbations by measuring information-theoretic divergence between condition-specific gene interaction network configurations. In simulated stress tests and multiple experimental datasets, scDNS prioritizes key regulators and perturbed cell populations, even when expression changes are minimal but network rewiring is pronounced. Applications to immunodeficiency mutations, stimulus responses, and viral infection reveal hidden regulatory programs and heterogeneous responder cell states. In pancreatic cancer, scDNS nominates TIMM44 as a mitochondrial sensitizer enhancing gemcitabine efficacy. Together, scDNS provides a powerful tool for inferring dynamic gene perturbations in single cells.