The spike‐and‐slab quantile LASSO for robust variable selection in cancer genomics studies

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 nonrobust ones to identify important genes associated...

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Published in:Statistics in medicine Vol. 43; no. 26; pp. 4928 - 4983
Main Authors: Liu, Yuwen, Ren, Jie, Ma, Shuangge, Wu, Cen
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 20.11.2024
Wiley Subscription Services, Inc
Subjects:
Bayes Theorem
Cancer
Computer Simulation
expectation‐maximization (EM) algorithm
Genomics
Genomics - methods
Humans
Likelihood Functions
Lung cancer
Lung Neoplasms - genetics
Melanoma - genetics
Models, Statistical
Neoplasms - genetics
quantile LASSO
regularized Bayesian quantile regression
robust variable selection
Skin Neoplasms - genetics
spike‐and‐slab prior
expectation‐maximization (EM) algorithm
robust variable selection
quantile LASSO
regularized Bayesian quantile regression
spike‐and‐slab prior
ISSN:0277-6715, 1097-0258, 1097-0258
Online Access:Get full text
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Summary: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 nonrobust 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 (Roc̆ková and George, J Am Stat Associat, 2018, 113(521): 431–444). 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 nondifferentiable 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).
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ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.10196