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Benchmarking over 70 single-cell trajectory inference tools revealed that method selection depends on dataset dimensions and trajectory topology. A new evaluation pipeline aids users in choosing the best tool for their specific single-cell omics data analysis.

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Area of Science:

  • Computational biology
  • Single-cell genomics
  • Bioinformatics

Background:

  • Trajectory inference methods computationally predict cell differentiation pathways from single-cell omics data.
  • Over 70 tools exist, but direct performance comparison is difficult due to varied inputs and outputs.

Purpose of the Study:

  • To benchmark 45 trajectory inference tools on diverse datasets.
  • To provide guidelines for selecting appropriate tools based on dataset characteristics.
  • To facilitate the development of improved single-cell analysis tools.

Main Methods:

  • Evaluated 45 trajectory inference tools using 110 real and 229 synthetic datasets.
  • Assessed performance based on cellular ordering, topology, scalability, and usability.
  • Developed a publicly available data and evaluation pipeline.

Main Results:

  • No single tool excels across all scenarios; tool performance is dataset-dependent.
  • Complementarity of existing tools was observed.
  • Dataset dimensions and trajectory topology are key factors in tool selection.

Conclusions:

  • Selecting the optimal trajectory inference tool requires careful consideration of dataset properties.
  • The developed benchmark and guidelines will aid researchers in analyzing complex single-cell data.
  • The evaluation pipeline supports future advancements in single-cell trajectory inference.