A double-robust test for high-dimensional gene coexpression networks conditioning on clinical information.

ABSTRACT: It has been increasingly appealing to evaluate whether expression levels of two genes in a gene coexpression network are still dependent given samples' clinical information, in which the conditional independence test plays an essential role. For enhanced robustness regarding model assumptions, we propose a class of double-robust tests for evaluating the dependence of bivariate outcomes after controlling for known clinical information. Although the proposed test relies on the marginal density functions of bivariate outcomes given clinical information, the test remains valid as long as one of the density functions is correctly specified. Because of the closed-form variance formula, the proposed test procedure enjoys computational efficiency without requiring a resampling procedure or tuning

SUBMITTER: Ding M 

PROVIDER: S-EPMC10838184 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature