Abstract
Lipid species are critical components of eukaryotic membranes. They play key roles in many biological processes such as signal transduction, cell homeostasis, and energy storage. Investigations of lipid–environment interactions, in addition to the lipid and environment main effects, have important implications in understanding the lipid metabolism and related changes in phenotype. In this study, we developed a novel penalized variable selection method to identify important lipid–environment interactions in a longitudinal lipidomics study. An efficient Newton–Raphson based algorithm was proposed within the generalized estimating equation (GEE) framework. We conducted extensive simulation studies to demonstrate the superior performance of our method over alternatives, in terms of both identification accuracy and prediction performance. As weight control via dietary calorie restriction and exercise has been demonstrated to prevent cancer in a variety of studies, analysis of the high-dimensional lipid datasets collected using 60 mice from the skin cancer prevention study identified meaningful markers that provide fresh insight into the underlying mechanism of cancer preventive effects.
Highlights
Longitudinal data are frequently observed in a diversity of scientific research areas, including economics, biomedical studies, and clinical trials
To accommodate simultaneously the selection of individual and group structure corresponding to the main lipid effect and interaction effect respectively, we propose a novel penalized variable selection method for longitudinal clustered data
Since the minimax concave penalty (MCP) is incorporated as the baseline penalty in A1–A3, A4–A6 have been developed based on MCP and used as benchmark methods for comparison
Summary
Longitudinal data are frequently observed in a diversity of scientific research areas, including economics, biomedical studies, and clinical trials. A common characteristic of the longitudinal data is that the same subject is measured repeatedly over a certain period of time; the repeated measurements are correlated. Many modeling techniques have been proposed to accommodate the multivariate correlated nature of the data [1,2]. The emergence of new types of data has brought constant challenges to the development of novel statistical methods for longitudinal studies. As penalization has been demonstrated as an effective way for conducting variable selection in linear and generalized linear models with a univariate response [3,4], substantial efforts have been devoted to developing penalized variable selection methods with longitudinal responses, such [5,6,7], among many others
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