Controlling false discovery rate for mediator selection in high-dimensional data

The need to select mediators from a high dimensional data source, such as neuroimaging data and genetic data, arises in much scientific research. In this work, we formulate a multiple-hypothesis testing framework for mediator selection from a high-dimensional candidate set, and propose a method, which extends the recent development in false discovery rate (FDR)-controlled variable selection with knockoff to select mediators with FDR control. We show that the proposed method and algorithm achieved finite sample FDR control. We present extensive simulation results to demonstrate the power and finite sample performance compared with the existing method. Lastly, we demonstrate the method for analyzing the Adolescent Brain Cognitive Development (ABCD) study, in which the proposed method selects several resting-state functional magnetic resonance imaging connectivity markers as mediators for the relationship between adverse childhood events and the crystallized composite score in the NIH toolbox.

False discovery rate-controlled multiple testing for union null hypotheses: a knockoff-based approach

False discovery rate (FDR) controlling procedures provide important statistical guarantees for replicability in signal identification based on multiple hypotheses testing. In many fields of study, FDR controling procedures are used in high-dimensional (HD) analyses to discover features that are truly associated with the outcome. In some recent applications, data on the same set of candidate features are independently collected in multiple different studies. For example, gene expression data are collected at different facilities and with different cohorts, to identify the genetic biomarkers of multiple types of cancers. These studies provide us with opportunities to identify signals by considering information from different sources (with potential heterogeneity) jointly. This paper is about how to provide FDR control guarantees for the tests of union null hypotheses of conditional independence. We present a knockoff-based variable selection method (Simultaneous knockoffs) to identify mutual signals from multiple independent datasets, providing exact FDR control guarantees under finite sample settings. This method can work with very general model settings and test statistics. We demonstrate the performance of this method with extensive numerical studies and two real-data examples.

Deep-gKnock: Nonlinear group-feature selection with deep neural networks

Feature selection is central to contemporary high-dimensional data analysis. Group structure among features arises naturally in various scientific problems. Many methods have been proposed to incorporate the group structure information into feature selection. However, these methods are normally restricted to a linear regression setting. To relax the linear constraint, we design a new Deep Neural Network (DNN) architecture and integrating it with the recently proposed knockoff technique to perform nonlinear group-feature selection with controlled group-wise False Discovery Rate (gFDR). Experimental results on high-dimensional synthetic data demonstrate that our method achieves the highest power and accurate gFDR control compared with state-of-the-art methods. The performance of Deep-gKnock is especially superior in the following five situations: (1) nonlinearity relationship; (2) dimension p greater than sample size n; (3) high between-group correlation; (4) high within-group correlation; (5) large number of associated groups. And Deep-gKnock is also demonstrated to be robust to the misspecification of the feature distribution and the change of network architecture. Moreover, Deep-gKnock achieves scientifically meaningful group-feature selection results for cutting-edge real world datasets.