Bipartite graph-based approach for clustering of cell lines by gene expression-drug response associations.

ABSTRACT:

Motivation

In pharmacogenomic studies, the biological context of cell lines influences the predictive ability of drug-response models and the discovery of biomarkers. Thus, similar cell lines are often studied together based on prior knowledge of biological annotations. However, this selection approach is not scalable with the number of annotations, and the relationship between gene-drug association patterns and biological context may not be obvious.

Results

We present a procedure to compare cell lines based on their gene-drug association patterns. Starting with a grouping of cell lines from biological annotation, we model gene-drug association patterns for each group as a bipartite graph between genes and drugs. This is accomplished by applying sparse canonical correlation

SUBMITTER: Chi C 

PROVIDER: S-EPMC8428606 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature