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On the Stability of Feature Selection in Multiomics Data

Feature selection is a prominent activity when dealing with classification/regression problems in biological and omics data. Despite the effort devoted to this issue theoretically, feature selection stability within and across methods is often overlooked in practice. This is a compelling issue because a unique or at least stable answer is needed in clinical scenarios. Here, we analyse in detail a multiomics small sample data set, the Oxford Street II data set, and discuss how existing methods perform in terms of the usual metrics but also in terms of intra and inter feature selection stability. To mitigate the observed instability, we propose a simple unsupervised feature prefiltering, achieving promising results.