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Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model

Author

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  • José Carbonell-Caballero

    (Barcelona Supercomputing Center, Life Sciences Department, 08034 Barcelona, Spain)

  • Antonio López-Quílez

    (Estadística e investigación Operativa, Universitat de València, 46100 Burjassot, Spain)

  • David Conesa

    (Estadística e investigación Operativa, Universitat de València, 46100 Burjassot, Spain)

  • Joaquín Dopazo

    (Clinical Bioinformatics Area, Fundación Progreso y Salud, Hospital Virgen del Rocio, 46100 Sevilla, Spain
    Functional Genomics Node (INB), Fundación Progreso y Salud, Hospital Virgen del Rocio, 46100 Sevilla, Spain
    Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Fundación Progreso y Salud, Hospital Virgen del Rocio, 46100 Sevilla, Spain)

Abstract

Genomic heterogeneity constitutes one of the most distinctive features of cancer diseases, limiting the efficacy and availability of medical treatments. Tumorigenesis emerges as a strongly stochastic process, producing a variable landscape of genomic configurations. In this context, matrix factorisation techniques represent a suitable approach for modelling such complex patterns of variability. In this work, we present a hierarchical factorisation model conceived from a systems biology point of view. The model integrates the topology of molecular pathways, allowing to simultaneously factorise genes and pathways activity matrices. The protocol was evaluated by using simulations, showing a high degree of accuracy. Furthermore, the analysis with a real cohort of breast cancer patients depicted the internal composition of some of the most relevant altered biological processes in the disease, describing gene and pathway level strategies and their observed combinations in the population of patients. We envision that this kind of approaches will be essential to better understand the hallmarks of cancer.

Suggested Citation

  • José Carbonell-Caballero & Antonio López-Quílez & David Conesa & Joaquín Dopazo, 2021. "Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model," Mathematics, MDPI, vol. 9(21), pages 1-24, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2833-:d:674606
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
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