In contrast to feature selection and gene set analysis, bi-level selection is a process of selecting not only important gene sets but also important genes within those gene sets. Depending on the order of selections, a bi-level selection method can be classified into three categories - forward selection, which first selects relevant gene sets followed by the selection of relevant individual genes; backward selection which takes the reversed order; and simultaneous selection, which performs the two tasks simultaneously usually with the aids of a penalized regression model. To test the existence of subtypespecific prognostic genes for non-small cell lung cancer (NSCLC), we had previously proposed the Cox-filter method that examines the association between patients' survival time after diagnosis with one specific gene, the disease subtypes, and their interaction terms. In this study, we further extend it to carry out forward and backward bi-level selection. Using simulations and a NSCLC application, we demonstrate that the forward selection outperforms the backward selection and other relevant algorithms in our setting. Both proposed methods are readily understandable and interpretable. Therefore, they represent useful tools for the researchers who are interested in exploring the prognostic value of gene expression data for specific subtypes or stages of a disease.
[1]Jilin Univ, Sch Math, Ctr Appl Stat Res, 2699 Qianjin St, Changchun 130012, Jilin, Peoples R China.
[2]Jilin Univ, Hosp 1, Div Clin Res, 71 Xinmin St, Changchun 130021, Jilin, Peoples R China.
[3]Univ Kentucky, Markey Canc Ctr, Dept Biostat, 800 Rose St, Lexington, KY 40536 USA.
[4]Emory Univ, Rollins Sch Publ Hlth, Dept Biostat & Bioinformat, 1518 Clifton Rd NE, Atlanta, GA 30322 USA.
[5]Univ Missouri, Dept Stat, 146 Middlebush Hall, Columbia, MO 65211 USA.
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