ESCHR: a hyperparameter-randomized ensemble approach for robust clustering across diverse datasets.

Area of Science:

• Computational biology
• Bioinformatics
• Data science

Background:

• Clustering is essential for single-cell analysis but current methods face limitations in accuracy, robustness, usability, and interpretability.
• Existing techniques often require extensive hyperparameter tuning, hindering widespread adoption and reliable application.

Purpose of the Study:

• To develop an advanced ensemble clustering method that overcomes the limitations of current single-cell analysis techniques.
• To improve the accuracy, robustness, ease of use, and interpretability of clustering in single-cell data.

Main Methods:

• Developed a novel hyperparameter-randomized ensemble clustering approach.
• Applied the method to perform both hard clustering and soft clustering to identify continuum-like regions.
• Demonstrated the method's utility in mapping connectivity and transitions between distinct cell populations.

Main Results:

• The ensemble clustering method demonstrated superior performance in hard clustering compared to existing methods.
• The approach effectively characterized continuum-like regions and quantified clustering uncertainty through soft clustering.
• Successfully mapped complex relationships, including transitions between MNIST handwritten digits and hypothalamic tanycyte subpopulations.

Conclusions:

• The proposed hyperparameter-randomized ensemble clustering significantly enhances accuracy, robustness, usability, and interpretability in single-cell analysis.
• This method offers a powerful tool for dissecting cellular heterogeneity and identifying transitional states.
• The approach shows potential applicability beyond single-cell biology, suggesting broader utility in data analysis.