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Augmented kurtosis-based projection pursuit: a novel, advanced machine learning approach for multi-omics data
Fabian Bong1,2, Ibrahim Ahmed1, Nithya Ramakrishnan1
1Faculty of Medicine, Department of Pharmacology, Laboratory of Integrative Multi-Omics Research, Dalhousie University, Halifax, NSB3H 4R2, Canada.
Nucleic Acids Research
|September 19, 2025
Summary
This study introduces kurtosis-based projection pursuit analysis with classification and regression trees (kPPA-CART) for multi-omics data. kPPA-CART effectively identifies biological significance in breast cancer subtypes and survival, outperforming existing methods.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Multi-omics data presents challenges due to heterogeneity and large dynamic ranges.
- Existing analysis tools often struggle with low-intensity features or require large biological effect sizes.
- Extracting maximum information potential from complex biological datasets remains a significant hurdle.
Purpose of the Study:
- To benchmark existing multi-omics data analysis tools.
- To introduce kurtosis-based projection pursuit analysis augmented with classification and regression trees (kPPA-CART) as a robust alternative.
- To demonstrate kPPA-CART's superiority in inferring biological significance from challenging datasets.
Main Methods:
- Comprehensive benchmarking of multi-omics data analysis tools.
- Development and application of kurtosis-based projection pursuit analysis with classification and regression trees (kPPA-CART).
- Utilized ground truth data, The Cancer Genome Atlas (TCGA) breast cancer data, and AURORA US consortium metastatic breast cancer data.
Main Results:
- kPPA-CART demonstrated superior performance in inferring biological significance from low-intensity features and small effect sizes.
- Application to TCGA data identified novel genes clustering breast cancer samples into subtypes mimicking PAM50 classes with improved accuracy.
- Validation on AURORA US data revealed genes associated with poor event-free survival and tumor mutational burden.
Conclusions:
- kPPA-CART offers a robust and easy-to-implement solution for multi-omics data analysis.
- The method successfully identifies novel biomarkers and subtypes in breast cancer.
- An R package and online implementation of kPPA-CART are provided for broader accessibility.
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