Effect of hyperparameters on variable selection in random forests

Random forests (RFs) are well suited for prediction modeling and variable selection in high-dimensional omics studies. The effect of hyperparameters of the RF algorithm on prediction performance and variable importance estimation have previously been investigated. However, how hyperparameters impact RF-based variable selection remains unclear. We evaluate the effects on the Vita and the Boruta variable selection procedures based on two simulation studies utilizing theoretical distributions and empirical gene expression data. We assess the ability of the procedures to select important variables (sensitivity) while controlling the false discovery rate (FDR). Our results show that the proportion of splitting candidate variables (mtry.prop) and the sample fraction (sample.fraction) for the training dataset influence the selection procedures more than the drawing strategy of the training datasets and the minimal terminal node size. A suitable setting of the RF hyperparameters depends on the correlation structure in the data. For weakly correlated predictor variables, the default value of mtry is optimal, but smaller values of sample.fraction result in larger sensitivity. In contrast, the difference in sensitivity of the optimal compared to the default value of sample.fraction is negligible for strongly correlated predictor variables, whereas smaller values than the default are better in the other settings. In conclusion, the default values of the hyperparameters will not always be suitable for identifying important variables. Thus, adequate values differ depending on whether the aim of the study is optimizing prediction performance or variable selection.

Empowering individual trait prediction using interactions

One component of precision medicine is to construct prediction models with their predictive ability as high as possible, e.g. to enable individual risk prediction. In genetic epidemiology, complex diseases have a polygenic basis and a common assumption is that biological and genetic features affect the outcome under consideration via interactions. In the case of omics data, the use of standard approaches such as generalized linear models may be suboptimal and machine learning methods are appealing to make individual predictions. However, most of these algorithms focus mostly on main or marginal effects of the single features in a dataset. On the other hand, the detection of interacting features is an active area of research in the realm of genetic epidemiology. One big class of algorithms to detect interacting features is based on the multifactor dimensionality reduction (MDR). Here, we extend the model-based MDR (MB-MDR), a powerful extension of the original MDR algorithm, to enable interaction empowered individual prediction. Using a comprehensive simulation study we show that our new algorithm can use information hidden in interactions more efficiently than two other state-of-the-art algorithms, namely the Random Forest and Elastic Net, and clearly outperforms these if interactions are present. The performance of these algorithms is comparable if no interactions are present. Further, we show that our new algorithm is applicable to real data by comparing the performance of the three algorithms on a dataset of rheumatoid arthritis cases and healthy controls. As our new algorithm is not only applicable to biological/genetic data but to all datasets with discrete features, it may have practical implications in other applications as well, and we made our method available as an R package.