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Singular Value Decomposition-Driven Non-negative Matrix Factorization with Application to Identify the Association

Jin Deng1,2, Kaijun Li1, Wei Luo3,4

  • 1College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China.

Interdisciplinary Sciences, Computational Life Sciences
|February 29, 2024
PubMed
Summary

This study introduces a novel SVD-NMF model to analyze multi-omics data for understanding sarcoma recurrence. The model reveals key gene-pathway-cell associations and potential biomarkers for personalized sarcoma treatments.

Keywords:
Non-negative matrix factorizationPathwayRecurrenceSarcomaSingular value decomposition

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Area of Science:

  • Oncology
  • Bioinformatics
  • Genomics

Background:

  • Sarcomas are complex, diverse malignant tumors with high recurrence rates.
  • Understanding sarcoma recurrence mechanisms is crucial for developing personalized treatments.
  • Previous multi-modal data analyses overlooked gene interactions within signaling pathways.

Purpose of the Study:

  • To identify association patterns of gene-pathway-cell data related to sarcoma recurrence.
  • To develop a reproducible multi-modal data fusion method for sarcoma research.
  • To uncover potential biomarkers and mechanisms of sarcoma recurrence.

Main Methods:

  • Collected and integrated whole-slide images, gene expression, and pathway data from over 260 sarcoma samples (UCSC, TCGA).
  • Developed a singular value decomposition (SVD)-driven joint non-negative matrix factorization (NMF) model for reproducible multi-modal data fusion.
  • Applied the SVD-NMF model to analyze imaging, gene, and pathway data for associations with sarcoma recurrence.

Main Results:

  • The SVD-NMF model demonstrated enhanced performance and reproducibility compared to standard NMF.
  • Identified significant relationships between genes in pathways and cellular image features.
  • Uncovered potential biomarkers and elucidated mechanisms of sarcoma recurrence from an imaging-genetics perspective.

Conclusions:

  • The SVD-NMF model offers a novel approach for integrating multi-omics data to study sarcoma recurrence.
  • Multi-level analysis provides valuable insights into biological processes, cellular features, and sarcoma recurrence.
  • Findings contribute to understanding sarcoma heterogeneity and developing targeted therapeutic strategies.