Topology constrained nonnegative matrix factorization for time varying omic expression.

Area of Science:

• Computational Biology
• Bioinformatics
• Systems Biology

Background:

• Analyzing high-dimensional omic data for disease progression is challenging with small sample sizes.
• Traditional biomarker discovery is expensive and limited; Nonnegative Matrix Factorization (NMF) lacks stability and biological relevance.

Purpose of the Study:

• To introduce TopConNMF, a robust topology-constrained NMF framework.
• To enhance stability, accuracy, and biological interpretability in omic data analysis.

Main Methods:

• Developed TopConNMF, incorporating structural constraints into NMF.
• Evaluated on two time-varying omic datasets with ground truths.
• Compared performance against conventional NMF and other state-of-the-art methods.

Main Results:

• TopConNMF demonstrated consistent stability and superior accuracy across datasets.
• Achieved biologically relevant factorization compared to benchmark methods.
• Confirmed robustness in capturing disease-specific profiles and efficiency with high-dimensional data.

Conclusions:

• TopConNMF provides stable, interpretable factorization for deeper understanding of biological systems.
• Its broad applicability aids omic data analysis and biomarker discovery.
• Facilitates reliable biomarker discovery from limited omic data for clinical applications.