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

by Selleckchem | Mar 30 2023

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 functional form of the dependence between the responses and the perturbations and it enjoys finite sample false discovery rate control for the selected set of important gene expression responses. We apply this approach to the Library of Integrated Network-Based Cellular Signature data sets which is a National Institutes of Health Common Fund program that catalogs how human cells globally respond to chemical, genetic and disease perturbations. We identified important genes whose expression is directly modulated in response to perturbation with anthracycline, vorinostat, trichostatin-a, geldanamycin and sirolimus. We compare the set of important genes that respond to these small molecules to identify co-responsive pathways. Identification of which genes respond to specific perturbation stressors can provide better understanding of the underlying mechanisms of disease and advance the identification of new drug targets.

Comments：

The method described in this statement is aimed at identifying significant gene expression changes in multiple perturbation experiments. This is a challenging problem because it requires identifying the most important genes among a large number of potential variables, while also accounting for the nonlinear relationship between gene expression and the perturbation. The approach presented here uses the model-X knockoffs framework and Deep Neural Networks, which allows for finite sample false discovery rate control for the selected set of important gene expression responses.

The approach was applied to the Library of Integrated Network-Based Cellular Signature (LINCS) dataset, which catalogs how human cells respond to various chemical, genetic, and disease perturbations. By identifying important genes that are directly modulated in response to specific perturbation stressors (such as anthracycline, vorinostat, trichostatin-a, geldanamycin, and sirolimus), the researchers were able to gain a better understanding of the underlying mechanisms of disease and advance the identification of new drug targets.

Overall, the approach described here provides a valuable tool for identifying significant gene expression changes in large-scale multiple perturbation experiments, which can help researchers gain insights into the complex molecular pathways that respond to genetic and environmental changes.