The Spike-and-Slab Quantile LASSO for Robust Variable Selection in Cancer Genomics Studies

TL;DR

This paper introduces a robust Bayesian variable selection method, the spike-and-slab quantile LASSO, tailored for high-dimensional cancer genomics data with outliers and heavy tails, demonstrating superior performance in simulations and real case studies.

Contribution

It develops a novel spike-and-slab quantile LASSO that combines Bayesian regularization with robust likelihood, enabling effective variable selection in complex genomics data.

Findings

Outperforms competing methods in simulations with heavy-tailed errors.

Efficient posterior mode estimation via EM and soft-thresholding.

Proven effectiveness in lung and skin cancer genomics case studies.

Abstract

Data irregularity in cancer genomics studies has been widely observed in the form of outliers and heavy-tailed distributions in the complex traits. In the past decade, robust variable selection methods have emerged as powerful alternatives to the non-robust ones to identify important genes associated with heterogeneous disease traits and build superior predictive models. In this study, to keep the remarkable features of the quantile LASSO and fully Bayesian regularized quantile regression while overcoming their disadvantage in the analysis of high-dimensional genomics data, we propose the spike-and-slab quantile LASSO through a fully Bayesian spike-and-slab formulation under the robust likelihood by adopting the asymmetric Laplace distribution (ALD). The proposed robust method has inherited the prominent properties of selective shrinkage and self-adaptivity to the sparsity pattern from the spike-and-slab LASSO (Ro\v{c}kov\'a and George, 2018). Furthermore, the spike-and-slab quantile LASSO has a computational advantage to locate the posterior modes via soft-thresholding rule guided Expectation-Maximization (EM) steps in the coordinate descent framework, a phenomenon rarely observed for robust regularization with non-differentiable loss functions. We have conducted comprehensive simulation studies with a variety of heavy-tailed errors in both homogeneous and heterogeneous model settings to demonstrate the superiority of the spike-and-slab quantile LASSO over its competing methods. The advantage of the proposed method has been further demonstrated in case studies of the lung adenocarcinomas (LUAD) and skin cutaneous melanoma (SKCM) data from The Cancer Genome Atlas (TCGA).