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<p>Biological insights often depend on comparing conditions such as disease and health. Yet, we lack effective computational tools for integrating single-cell genomics data across conditions or characterizing transitions from normal to deviant cell states. Here, we present Decipher, a deep generative model that characterizes derailed cell-state trajectories. Decipher jointly models and visualizes gene expression and cell state from normal and perturbed single-cell RNA-seq data, revealing shared and disrupted dynamics. We demonstrate its superior performance across diverse contexts, including in pancreatitis with oncogene mutation, acute myeloid leukemia, and gastric cancer.</p>

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Joint representation and visualization of derailed cell states with Decipher

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月23日

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  • Achille Nazaret,
  • Joy Linyue Fan,
  • Vincent-Philippe Lavallée,
  • Cassandra Burdziak,
  • Andrew E. Cornish,
  • Vaidotas Kiseliovas,
  • Robert L. Bowman,
  • Ignas Masilionis,
  • Jaeyoung Chun,
  • Shira E. Eisman,
  • James Wang,
  • Justin Hong,
  • Lingting Shi,
  • Ross L. Levine,
  • Linas Mazutis,
  • David Blei,
  • Dana Pe’er,
  • Elham Azizi

摘要

Biological insights often depend on comparing conditions such as disease and health. Yet, we lack effective computational tools for integrating single-cell genomics data across conditions or characterizing transitions from normal to deviant cell states. Here, we present Decipher, a deep generative model that characterizes derailed cell-state trajectories. Decipher jointly models and visualizes gene expression and cell state from normal and perturbed single-cell RNA-seq data, revealing shared and disrupted dynamics. We demonstrate its superior performance across diverse contexts, including in pancreatitis with oncogene mutation, acute myeloid leukemia, and gastric cancer.