GEE-TGDR: A longitudinal feature selection algorithm and its application to lncRNA expression profiles for psoriasis patients treated with immune therapies

With the fast evolution of high-throughput technology, longitudinal gene expression experiments have become affordable and increasingly common in biomedical fields. Generalized estimating equation (GEE) approach is a widely used statistical method for the analysis of longitudinal data. Feature selection is imperative in longitudinal omics data analysis. Among a variety of existing feature selection methods, an embedded method, namely, threshold gradient descent regularization (TGDR) stands out due to its excellent characteristics. An alignment of GEE with TGDR is a promising area for the purpose of identifying relevant markers that can explain the dynamic changes of outcomes across time. In this study, we proposed a new novel feature selection algorithm for longitudinal outcomes:GEE-TGDR. In the GEE-TGDR method, the corresponding quasi-likelihood function of a GEE model is the objective function to be optimized and the optimization and feature selection are accomplished by the TGDR method. We applied the GEE-TGDR method a longitudinal lncRNA gene expression dataset that examined the treatment response of psoriasis patients to immune therapy. Under different working correlation structures, a list including 10 relevant lncRNAs were identified with a predictive accuracy of 80 % and meaningful biological interpretation. To conclude, a widespread application of the proposed GEE-TGDR method in omics data analysis is anticipated.

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