Abstract
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 subtype-specific 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.
Highlights
In contrast to feature selection and gene set analysis, bi-level selection is a process of selecting important gene sets and important genes within those gene sets
Based on the order of selection on genes and gene sets, bi-level selection methods can be classified into three categories: (1) the forward selection method which first selects relevant gene sets followed by the selection of relevant individual genes, e.g.,4; (2) the backward selection method such as[5,6,7] which takes the reversed order; and (3) the simultaneous selection method which selects important gene sets and genes at the same time, usually by the means of adding a penalty term which penalizes all genes inside the same group e.g.,3,8
Similar to the simulation setting in our previous study, we considered two extreme cases: (1) the mutually exclusive prognostic genes case in which AC and squamous cell carcinoma (SCC) have completely distinct sets of survival-relevant genes, and (2) no subtype-specific prognostic genes case in which AC and SCC share identical prognostic markers
Summary
In contrast to feature selection and gene set analysis, bi-level selection is a process of selecting important gene sets and important genes within those gene sets. 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. Based on the order of selection on genes and gene sets, bi-level selection methods can be classified into three categories: (1) the forward selection method which first selects relevant gene sets followed by the selection of relevant individual genes, e.g.,4; (2) the backward selection method such as[5,6,7] which takes the reversed order; and (3) the simultaneous selection method which selects important gene sets and genes at the same time, usually by the means of adding a penalty term which penalizes all genes inside the same group e.g.,3,8. We focus on the forward and backward selection methods due to their ease in implementation and wide utilization
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