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Data clustering with improved expectation maximization for multiomics data integration
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  • Joseph Pateras ,
  • Musaddiq Lodi ,
  • Pratip Rana ,
  • Preetam Ghosh
Joseph Pateras
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Musaddiq Lodi
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Pratip Rana
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Preetam Ghosh
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For studying cancer biology, multiomics data integration has become an invaluable tool. With the amount of -omics data, its availability and the pace of its acquisition increasing rapidly, multiomics data integration is even more pivotal. This work employs a novel adaptation of the expectation maximization routine for joint latent variable modeling of multiomics patient profiles. Along with traditional methods of biological feature selection, the data-centric approach toward latent distribution optimization can adequately cluster patients from well-studied cancer types and does so with lower computational expense. Crucially, this work modifies the optimization subroutines in the relatively standard joint latent variable -omics workflow for improved survival analysis and run-time performance. This work also provides a framework that identifies distinctions between cancer subtypes and proposes potential biomarkers for breast cancer.