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.
https://hal.archives-ouvertes.fr/hal-02968360 Contributor : Denis LaloëConnect 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
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Distributed under a Creative Commons Attribution 4.0 International License
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⟩