Individualized multi omic pathway deviation scores using multiple factor analysis

Dc971cfc929cb925baf3d41f48e25fa5?s=47 Andrea Rau
December 05, 2019

Individualized multi omic pathway deviation scores using multiple factor analysis

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.

Dc971cfc929cb925baf3d41f48e25fa5?s=128

Andrea Rau

December 05, 2019
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