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ABSTRACT In modern statistical applications, identifying critical features in high‐dimensional data is essential for scientific discoveries. Traditional best subset selection methods face computational challenges, while regularization approaches such as Lasso, SCAD and their variants often exhibit poor performance with ultrahigh‐dimensional data. Sure screening methods, widely used for dimensionality reduction, have been developed as popular alternatives, but few target heavy‐tailed characteristics in modern big data. This paper introduces a new sure screening method, based on robust distance correlation (‘RDC’), designed for heavy‐tailed data. The proposed method inherits the benefits of the original model‐free distance correlation‐based screening while robustly estimating distance correlation in the presence of heavy‐tailed data. We further develop an FDR control procedure by incorporating the Reflection via Data Splitting (REDS) method. Extensive simulations demonstrate the method's advantage over existing screening procedures under different scenarios of heavy‐tailedness. Its application to high‐dimensional heavy‐tailed RNA‐seq data from The Cancer Genome Atlas (TCGA) pancreatic cancer cohort showcases superior performance in identifying biologically meaningful genes predictive of MAPK1 protein expression critical to pancreatic cancer. 

Abstract MotivationThe advancement of high-throughput technology characterizes a wide variety of epigenetic modifications and noncoding RNAs across the genome involved in disease pathogenesis via regulating gene expression. The high dimensionality of both epigenetic/noncoding RNA and gene expression data make it challenging to identify the important regulators of genes. Conducting univariate test for each possible regulator–gene pair is subject to serious multiple comparison burden, and direct application of regularization methods to select regulator–gene pairs is computationally infeasible. Applying fast screening to reduce dimension first before regularization is more efficient and stable than applying regularization methods alone. ResultsWe propose a novel screening method based on robust partial correlation to detect epigenetic and noncoding RNA regulators of gene expression over the whole genome, a problem that includes both high-dimensional predictors and high-dimensional responses. Compared to existing screening methods, our method is conceptually innovative that it reduces the dimension of both predictor and response, and screens at both node (regulators or genes) and edge (regulator–gene pairs) levels. We develop data-driven procedures to determine the conditional sets and the optimal screening threshold, and implement a fast iterative algorithm. Simulations and applications to long noncoding RNA and microRNA regulation in Kidney cancer and DNA methylation regulation in Glioblastoma Multiforme illustrate the validity and advantage of our method. Availability and implementationThe R package, related source codes and real datasets used in this article are provided at https://github.com/kehongjie/rPCor. Supplementary informationSupplementary data are available at Bioinformatics online. 

Abstract Detection of prognostic factors associated with patients’ survival outcome helps gain insights into a disease and guide treatment decisions. The rapid advancement of high-throughput technologies has yielded plentiful genomic biomarkers as candidate prognostic factors, but most are of limited use in clinical application. As the price of the technology drops over time, many genomic studies are conducted to explore a common scientific question in different cohorts to identify more reproducible and credible biomarkers. However, new challenges arise from heterogeneity in study populations and designs when jointly analyzing the multiple studies. For example, patients from different cohorts show different demographic characteristics and risk profiles. Existing high-dimensional variable selection methods for survival analysis, however, are restricted to single study analysis. We propose a novel Cox model based two-stage variable selection method called “Cox-TOTEM” to detect survival-associated biomarkers common in multiple genomic studies. Simulations showed our method greatly improved the sensitivity of variable selection as compared to the separate applications of existing methods to each study, especially when the signals are weak or when the studies are heterogeneous. An application of our method to TCGA transcriptomic data identified essential survival associated genes related to the common disease mechanism of five Pan-Gynecologic cancers.