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Individualized multi-omic pathway deviation scores using multiple factor analysis

View ORCID ProfileAndrea Rau, Regina Manansala, View ORCID ProfileMichael J. Flister, View ORCID ProfileHallgeir Rui, Florence Jaffrézic, Denis Laloë, View ORCID ProfilePaul L. Auer
doi: https://doi.org/10.1101/827022
Andrea Rau
1GABI, INRA, AgroParisTech, Universite Paris-Saclay, 78350, Jouy-en-Josas, France
2Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA
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  • ORCID record for Andrea Rau
Regina Manansala
2Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA
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Michael J. Flister
3Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Hallgeir Rui
3Department of Pathology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Florence Jaffrézic
1GABI, INRA, AgroParisTech, Universite Paris-Saclay, 78350, Jouy-en-Josas, France
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Denis Laloë
1GABI, INRA, AgroParisTech, Universite Paris-Saclay, 78350, Jouy-en-Josas, France
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  • For correspondence: denis.laloe@inra.fr pauer@uwm.edu
Paul L. Auer
2Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA
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  • ORCID record for Paul L. Auer
  • For correspondence: denis.laloe@inra.fr pauer@uwm.edu
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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.

Footnotes

  • Errors in citations in the Methods section have been corrected.

  • https://github.com/andreamrau/RMFRJLA_2019

  • https://github.com/andreamrau/padma

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 08, 2019.
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Individualized multi-omic pathway deviation scores using multiple factor analysis
Andrea Rau, Regina Manansala, Michael J. Flister, Hallgeir Rui, Florence Jaffrézic, Denis Laloë, Paul L. Auer
bioRxiv 827022; doi: https://doi.org/10.1101/827022
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Individualized multi-omic pathway deviation scores using multiple factor analysis
Andrea Rau, Regina Manansala, Michael J. Flister, Hallgeir Rui, Florence Jaffrézic, Denis Laloë, Paul L. Auer
bioRxiv 827022; doi: https://doi.org/10.1101/827022

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