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GRACKLE: an interpretable matrix factorization approach for biomedical representation learning
Lucas A Gillenwater1,2,3, Lawrence E Hunter4, James C Costello1,2,3,5
1Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, United States.
Bioinformatics (Oxford, England)
|July 15, 2025
Summary
GRACKLE, a novel method, enhances gene expression analysis by integrating molecular interactions and sample data. This approach improves disease gene signature identification, especially in complex cases with limited samples.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Gene expression disruptions are linked to diseases.
- Identifying disease-specific gene signatures is challenging due to co-occurring conditions and small sample sizes.
- Existing unsupervised learning methods lack clear biological explanations and do not integrate prior biological knowledge with sample labels.
Purpose of the Study:
- To develop a novel method for identifying disease-specific gene signatures by integrating prior biological knowledge.
- To improve the interpretability and accuracy of unsupervised learning in high-dimensional biological data.
- To address the limitations of current models in jointly considering molecular interactions and sample labels.
Main Methods:
- Introduced GRACKLE (Graph Regularization Across Contextual KnowLedgE), a nonnegative matrix factorization approach.
- Integrated sample similarity and gene similarity matrices using sample metadata and molecular relationships.
- Validated GRACKLE through simulation studies and application to breast tumor and Down syndrome datasets.
Main Results:
- GRACKLE outperformed other nonnegative matrix factorization algorithms, particularly under high background noise.
- Successfully stratified breast tumor samples and identified condition-enriched subgroups in individuals with Down syndrome.
- Latent representations generated by GRACKLE aligned with known biological patterns, including autoimmune conditions and sleep apnea in Down syndrome.
Conclusions:
- GRACKLE provides a robust solution for identifying context-specific molecular mechanisms in biomedical research.
- The model's flexibility allows application across various data modalities.
- GRACKLE enhances the understanding of gene expression in complex diseases and small sample settings.
