Overcoming the inadaptability of sparse group lasso for data with various group structures by stacking

Efficiently identifying genes based on gene expression level have been studied to help to classify different cancer types and improve the prediction performance. Logistic regression model based on regularization technique is often one of the effective approaches for simultaneously realizing prediction and feature (gene) selection in genomic data of high dimensionality. However, standard methods ignore biological group structure and generally result in poorer predictive models.

In this article, we develop a classifier named Stacked SGL that satisfies the criteria of prediction, stability and selection based on sparse group lasso penalty by stacking. Sparse group lasso has a mixing parameter representing the ratio of lasso to group lasso, thus providing a compromise between selecting a subset of sparse feature groups and introducing sparsity within each group. We propose to use stacked generalization to combine different ratios rather than choosing one ratio, which could help to overcome the inadaptability of sparse group lasso for some data. Considering that stacking weakens feature selection, we perform a post hoc feature selection which might slightly reduce predictive performance, but it shows superior in feature selection. Experimental results on simulation demonstrate that our approach enjoys competitive and stable classification performance and lower false discovery rate in feature selection for varying sets of data compared with other regularization methods. In addition, our method presents better accuracy in three public cancer datasets and identifies more powerful discriminatory and potential mutation genes for thyroid carcinoma.

The real data underlying this article are available from https://github.com/huanheaha/Stacked_SGL; https://zenodo.org/record/5761577#.YbAUyciEwk2.

Supplementary data are available at Bioinformatics online.