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Singular Value Decomposition-Driven Non-negative Matrix Factorization with Application to Identify the Association Patterns of Sarcoma Recurrence

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Abstract

Sarcomas are malignant tumors from mesenchymal tissue and are characterized by their complexity and diversity. The high recurrence rate making it important to understand the mechanisms behind their recurrence and to develop personalized treatments and drugs. However, previous studies on the association patterns of multi-modal data on sarcoma recurrence have overlooked the fact that genes do not act independently, but rather function within signaling pathways. Therefore, this study collected 290 whole solid images, 869 gene and 1387 pathway data of over 260 sarcoma samples from UCSC and TCGA to identify the association patterns of gene–pathway–cell related to sarcoma recurrences. Meanwhile, considering that most multi-modal data fusion methods based on the joint non-negative matrix factorization (NMF) model led to poor experimental repeatability due to random initialization of factorization parameters, the study proposed the singular value decomposition (SVD)-driven joint NMF model by applying the SVD method to calculate initialized weight and coefficient matrices to achieve the reproducibility of the results. The results of the experimental comparison indicated that the SVD algorithm enhances the performance of the joint NMF algorithm. Furthermore, the representative module indicated a significant relationship between genes in pathways and image features. Multi-level analysis provided valuable insights into the connections between biological processes, cellular features, and sarcoma recurrence. In addition, potential biomarkers were uncovered, while various mechanisms of sarcoma recurrence were identified from an imaging genetic perspective. Overall, the SVD–NMF model affords a novel perspective on combining multi-omics data to explore the association related to sarcoma recurrence.

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Data availability

The data that support the findings of this study are openly available in the Cancer Genome Atlas (TCGA) website (https://www.cancer.gov/) and the University of California Santa Cruz (UCSC) website (https://genome.ucsc.edu/).

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Funding

This work was supported in part by the Scientific and Technological Planning Project of Guangzhou City (No.202102020673, 2023A04J0316), the Young Scholar Project of Pazhou Lab (No.PZL2021KF0021), the Natural Science Foundation of Guangdong Province (No.2020A1515010813, 2414050002707), and the Young Innovation Talent Projects for Guangdong Universities (No. 2023KQNCX009).

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Authors and Affiliations

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

    Jin Deng, Kaijun Li & Wei Luo

  2. Pazhou Lab, Guangzhou, 510335, China

    Jin Deng & Wei Luo

Authors
  1. Jin Deng
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  2. Kaijun Li
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  3. Wei Luo
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Contributions

J. Deng: Conceptualization, methodology, data curation, writing–original draft preparation, writing–reviewing and editing, funding acquisition. K. Li: Software, writing–original draft preparation, visualization. W. Luo: Conceptualization, validation, writing–reviewing and editing, supervision, funding acquisition.

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Correspondence to Wei Luo.

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Deng, J., Li, K. & Luo, W. Singular Value Decomposition-Driven Non-negative Matrix Factorization with Application to Identify the Association Patterns of Sarcoma Recurrence. Interdiscip Sci Comput Life Sci (2024). https://doi.org/10.1007/s12539-024-00606-1

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