Using single-cell cytometry to illustrate integrated multi-perspective evaluation of clustering algorithms using...

Area of Science:

• Computational biology
• Bioinformatics
• Data science

Background:

• Automated gating algorithms are widely used for clustering cytometry and single-cell sequencing data.
• Current comparative evaluations often use single or independent metrics, leading to inconsistent results and lack of consensus on optimal algorithms.
• This inconsistency hinders the translatability of findings to novel datasets.

Purpose of the Study:

• To introduce the Pareto fronts framework as an integrative protocol for evaluating automated gating algorithms.
• To provide a more comprehensive and complete view of clustering performance by considering multiple metrics simultaneously.
• To minimize confounding factors from parameter selection and reveal algorithm tuning requirements.

Main Methods:

• The Pareto fronts framework leverages individual metrics as complementary perspectives to assess algorithm trade-offs.
• Latin Hypercube sampling is employed for systematic and broad sampling of algorithm parameter values.
• A comparative study of ChronoClust, FlowSOM, and Phenograph was conducted on four cytometry benchmark datasets using four performance metrics.

Main Results:

• The Pareto fronts framework provides a superior method for evaluating clustering performance by considering the trade-off between multiple metrics.
• Systematic parameter sampling minimizes confounding factors and highlights the tuning sensitivity of each algorithm.
• This study is the first to apply Pareto fronts for evaluating clustering algorithm performance in any domain.

Conclusions:

• The Pareto fronts framework offers a robust and integrative approach to algorithm evaluation in single-cell data analysis.
• This methodology enhances the understanding of algorithm performance and aids in selecting optimal tools for specific datasets.
• The framework promotes greater consensus and translatability of results in the field.