Interaction screening by Kendall’s partial correlation for ultrahigh-dimensional data with survival trait

In gene expression and genome-wide association studies, the identification of interaction effects is an important and challenging issue owing to its ultrahigh-dimensional nature. In particular, contaminated data and right-censored survival outcome make the associated feature screening even challenging.In this article, we propose an inverse probability-of-censoring weighted Kendall's tau statistic to measure association of a survival trait with biomarkers, as well as a Kendall's partial correlation statistic to measure the relationship of a survival trait with an interaction variable conditional on the main effects. The Kendall's partial correlation is then used to conduct interaction screening. Simulation studies under various scenarios are performed to compare the performance of our proposal with some commonly available methods. In the real data application, we utilize our proposed method to identify epistasis associated with the clinical survival outcomes of non-small-cell lung cancer, diffuse large B-cell lymphoma and lung adenocarcinoma patients. Both simulation and real data studies demonstrate that our method performs well and outperforms existing methods in identifying main and interaction biomarkers.R-package 'IPCWK' is available to implement this method, together with a reference manual describing how to perform the 'IPCWK' package.Supplementary data are available at Bioinformatics online.