RT Journal Article
SR Electronic
T1 Individualized multi-omic pathway deviation scores using multiple factor analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 827022
DO 10.1101/827022
A1 Andrea Rau
A1 Regina Manansala
A1 Michael J. Flister
A1 Hallgeir Rui
A1 Florence Jaffrézic
A1 Denis Laloë
A1 Paul L. Auer
YR 2019
UL http://biorxiv.org/content/early/2019/11/08/827022.abstract
AB 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.