Genomic data integration by WON-PARAFAC identifies interpretable factors for predicting drug-sensitivity in vivo.

ABSTRACT: Integrative analyses that summarize and link molecular data to treatment sensitivity are crucial to capture the biological complexity which is essential to further precision medicine. We introduce Weighted Orthogonal Nonnegative parallel factor analysis (WON-PARAFAC), a data integration method that identifies sparse and interpretable factors. WON-PARAFAC summarizes the GDSC1000 cell line compendium in 130 factors. We interpret the factors based on their association with recurrent molecular alterations, pathway enrichment, cancer type, and drug-response. Crucially, the cell line derived factors capture the majority of the relevant biological variation in Patient-Derived Xenograft (PDX) models, strongly suggesting our factors capture invariant and generalizable aspects of cancer biology. Fur

SUBMITTER: Kim Y 

PROVIDER: S-EPMC6834616 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature