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Individualized multi-omic pathway deviation scores using multiple factor analysis

Abstract : Malignant progression of normal tissue is typically driven by complex networks of somatic changes, including genetic mutations, copy number aberrations, epigenetic changes, and transcriptional reprogramming. To delineate aberrant multi-omic tumor features that correlate with clinical outcomes, we present a novel pathway-centric tool based on the multiple factor analysis framework called padma. Using a multi-omic consensus representation, padma quantifies and characterizes individualized pathway-specific multi-omic deviations and their underlying drivers, with respect to the sampled population. We demonstrate the utility of padma to correlate patient outcomes with complex genetic, epigenetic, and transcriptomic perturbations in clinically actionable pathways in breast and lung cancer.
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Contributor : Denis Laloë Connect in order to contact the contributor
Submitted on : Thursday, October 15, 2020 - 4:27:55 PM
Last modification on : Friday, August 5, 2022 - 2:38:11 PM


Distributed under a Creative Commons Attribution 4.0 International License

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Andrea Rau, Regina Manansala, Michael Flister, Hallgeir Rui, Florence Jaffrezic, et al.. Individualized multi-omic pathway deviation scores using multiple factor analysis. Biostatistics, Oxford University Press (OUP), 2020, pp.1-18. ⟨10.1093/biostatistics/kxaa029⟩. ⟨hal-02968360⟩



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