Identification of significant gene expression changes in multiple perturbation experiments using knockoffs.

ABSTRACT: Large-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. This problem is challenging because (i) the functional form of the nonlinear relationship between gene expression and the perturbation is unknown and (ii) identification of the most important genes is a high-dimensional variable selection problem. To deal with these challenges, we present here a method based on the model-X knockoffs framework and Deep Neural Networks to identify significant gene expression changes in multiple perturbation experiments. This approach makes no assumptions on the fun

SUBMITTER: Zhao T 

PROVIDER: S-EPMC10025447 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature