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scSurv: a deep generative model for single-cell survival analysis.

Chikara Mizukoshi1,2,3, Yasuhiro Kojima4, Shuto Hayashi1

  • 1Department of Computational and Systems Biology, Division of Biological Data Science, Medical Research Laboratory, Institute for Integrated Research, Institute of Science Tokyo, Tokyo 113-8510, Japan.

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scSurv quantifies how cell type differences impact cancer patient survival using single-cell data. This method identifies prognostic cells and genes, advancing precision oncology and disease outcome prediction.

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Area of Science:

  • Computational biology
  • Cancer research
  • Genomics

Background:

  • Single-cell omics reveals tumor cell heterogeneity.
  • Current methods lack single-cell resolution for linking heterogeneity to patient survival.
  • Understanding cellular contributions to outcomes is crucial for personalized medicine.

Purpose of the Study:

  • Introduce scSurv, a novel computational framework.
  • Quantify individual cellular contributions to clinical outcomes at single-cell resolution.
  • Integrate Cox proportional hazards models with deep generative models of single-cell transcriptomes.

Main Methods:

  • Developed scSurv, combining Cox proportional hazards model and deep generative model.
  • Applied scSurv to simulated and real single-cell omics datasets.
  • Validated accuracy and identified prognostic cells and genes.

Main Results:

  • scSurv accurately estimates cellular contributions to patient survival.
  • Identified cells and genes associated with favorable or adverse prognoses.
  • Reproduced known prognostic macrophage classifications in melanoma and enabled hazard mapping in renal cell carcinoma via spatial transcriptomics.

Conclusions:

  • scSurv provides a novel framework for analyzing single-cell data in relation to clinical outcomes.
  • The method advances understanding of tumor heterogeneity's impact on survival.
  • Demonstrated applicability across various cancers and infectious diseases, highlighting its versatility.