Optimizing clustering-based analytical methods with trimmed and sparse clustering.

Area of Science:

• Biomedical data analysis
• Computational biology
• Bioinformatics

Background:

• Clustering is vital for pattern discovery in complex biomedical data (gene expression, metabolomics, patient data).
• Traditional clustering methods struggle with noisy, high-dimensional, and outlier-prone biomedical datasets.
• Existing trimmed and sparse clustering methods require manual parameter tuning, leading to inefficiency and potential inaccuracies.

Purpose of the Study:

• To develop an automated trimmed and sparse clustering method for biomedical research.
• To address the challenges of determining the optimal number of clusters and input parameters.
• To enhance the usability, reproducibility, and accuracy of clustering in data-driven biomedical discoveries.

Main Methods:

• An automated trimmed and sparse clustering algorithm was developed.
• The method automatically determines the optimal number of clusters and tuning parameters (e.g., trimming proportion, sparsity level).
• The implementation is available via the evaluomeR R package for biomedical researchers.

Main Results:

• The automated method successfully determines optimal clustering parameters without manual intervention.
• It enhances robustness by effectively handling outliers and noise through trimmed and sparse approaches.
• The evaluomeR package provides an accessible tool for sophisticated clustering in biomedical research.

Conclusions:

• Automated trimmed and sparse clustering significantly improves the efficiency and reliability of biomedical data analysis.
• The evaluomeR package democratizes advanced clustering techniques for researchers.
• This advancement promotes more accurate and reproducible discoveries in the biomedical field.