A hyperparameter-randomized ensemble approach for robust clustering across diverse datasets.

Area of Science:

• Computational Biology
• Data Science
• Bioinformatics

Background:

• Clustering analysis is crucial for grouping similar objects, but current methods struggle with complex datasets like those from single-cell analysis.
• Existing clustering techniques often lack accuracy, robustness, ease of use, and interpretability.
• Manual hyperparameter selection in clustering can be a significant barrier to effective analysis.

Approach:

• Developed an ensemble clustering method incorporating hyperparameter randomization.
• This approach eliminates the need for manual hyperparameter tuning.
• The method generates both hard cluster labels and soft cluster memberships for nuanced data interpretation.

Key Points:

• Outperforms existing methods across diverse single-cell and synthetic datasets.
• Provides soft cluster memberships to identify continuum-like regions.
• Offers per-cell overlap scores to quantify assignment uncertainty.
• Demonstrates improved interpretability by visualizing intermediate stages in datasets like MNIST and hypothalamic tanycytes.

Conclusions:

• Enhances the quality of clustering for single-cell data.
• Improves the performance of subsequent downstream analyses.
• Presents a valuable tool for complex data analysis beyond single-cell research.