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Deep self-reconstruction driven joint nonnegative matrix factorization model for identifying multiple genomic imaging

Jin Deng1, Kai Wei2, Jiana Fang1

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

Journal of Biomedical Informatics
|June 27, 2024
PubMed
Summary

This study introduces a novel deep-self reconstructed joint nonnegative matrix factorization (DSRJNMF) model to uncover multimodal associations between histopathology images and transcriptomics data. The DSRJNMF model effectively identifies imaging genetic biomarkers for triple-negative breast cancer (TNBC).

Keywords:
Association patternGenetic variationHistopathology imageNonnegative matrix factorizationTriple negative breast cancer

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Area of Science:

  • Biomedical data science
  • Computational pathology
  • Genomics and transcriptomics

Background:

  • Multimodal data association studies are crucial for identifying biomarkers and understanding complex diseases.
  • Existing methods, often based on joint nonnegative matrix factorization, may not fully leverage the inherent multi-subspace structure of raw data.
  • This limitation can impact the accuracy of subsequent data integration and analysis.

Purpose of the Study:

  • To propose a novel deep-self reconstructed joint nonnegative matrix factorization (DSRJNMF) model for enhanced multimodal data association analysis.
  • To incorporate self-expressive properties for raw data reconstruction, capturing underlying similarity structures.
  • To integrate clinical prior information through sparsity, orthogonality, and regularization constraints for biologically relevant feature selection.

Main Methods:

  • Developed the DSRJNMF model, integrating deep self-representation with joint nonnegative matrix factorization.
  • Applied self-expressive properties to reconstruct raw data, preserving complex multi-subspace structures.
  • Incorporated sparsity, orthogonality, and regularization constraints derived from prior information into the model.

Main Results:

  • Successfully applied the DSRJNMF algorithm to identify imaging genetic associations in triple-negative breast cancer (TNBC).
  • Demonstrated superior estimation of associations between pathological image features and miRNA-gene expression.
  • Identified consistent multimodal imaging genetic biomarkers crucial for TNBC interpretation.

Conclusions:

  • The proposed DSRJNMF method offers a novel approach to data association analysis for complex diseases.
  • This approach effectively integrates diverse data modalities, enhancing biomarker discovery.
  • The findings provide valuable insights for guiding the interpretation and treatment of TNBC.