Identification of Significant Gene Expression Changes in Multiple Perturbation Experiments using Knockoffs

Abstract

MotivationLarge-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to genetic and environmental changes. A key question in these studies is which gene expression changes are important for the response to the perturbation. ResultsWe present here a method based on the model-X knockoffs framework to identify significant gene expression changes in multiple perturbation experiments. This approach makes no assumptions on the functional form of the dependence between the responses and the perturbations and provides finite sample false discovery rate control for the set of important gene expression responses. In a large-scale multiple perturbation gene expression data set from the Library of Integrated Network-Based Cellular Signature (LINCS) NIH program, we identified important genes whose expression is modulated in response to perturbation with anthracycline, vorinostat, trichostatin-a, geldanamycin, and sirolimus. Furthermore, we compared the set of important genes that respond to these small molecules to identify co-responsive pathways. Availability and Implementationhttps://github.com/flahertylab/deepYknockoff Contactpflaherty@umass.edu and zhaott0416@gmail.com Supplementary informationSupplementary data are available at Bioinformatics online.