Sceptic: pseudotime analysis for time-series single-cell sequencing and imaging data.

Journal: Genome biology
Published Date: Jul 17, 2025

Abstract

Several computational methods have been developed to construct single-cell pseudotime embeddings for extracting the temporal order of transcriptional cell states from time-series scRNA-seq datasets. However, existing methods suffer from low predictive accuracy, and this problem becomes even worse when we try to generalize to other data types such as scATAC-seq or microscopy images. To address this problem, we propose Sceptic, a support vector machine model for supervised pseudotime analysis. We demonstrate that Sceptic achieves significantly improved prediction power relative to state-of-the-art methods, and that Sceptic can be applied to a variety of single-cell data types, including single-nucleus image data.

Authors

  • Gang Li
    The Centre for Cyber Resilience and Trust, Deakin University, Australia.
  • Hyeon-Jin Kim
    Department of Genome Sciences, University of Washington, Seattle, 98115, USA.
  • Sriram Pendyala
    Department of Genome Sciences, University of Washington, Seattle, 98115, USA.
  • Ran Zhang
    Jiangsu Province Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, Jiangsu, 210009, China.
  • Jean-Philippe Vert
    MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, 77300 Fontainebleau, Institut Curie, 75248 Paris Cedex and INSERM U900, 75248 Paris Cedex, France.
  • Christine M Disteche
    Department of Laboratory Medicine and Pathology, University of Washington, Seattle, 98115, USA.
  • Xinxian Deng
    Department of Laboratory Medicine and Pathology, University of Washington, Seattle, 98115, USA.
  • Douglas M Fowler
    Department of Genome Sciences, University of Washington, Seattle, WA, USA.
  • William Stafford Noble
    1] Department of Computer Science and Engineering, University of Washington, 185 Stevens Way, Seattle, Washington 98195-2350, USA. [2] Department of Genome Sciences, University of Washington, 3720 15th Ave NE Seattle, Washington 98195-5065, USA.

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