The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Anne-Claire Haury (1, 2) , Pierre Gestraud (2) , Jean-Philippe Vert (1, 2)

1 CBIO - Centre de Bioinformatique
2 Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe

Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/.

Keywords: biomarker discovery; gene expression; microarrays; feature selection; signature; breast cancer

Domains: Quantitative Methods [q-bio.QM]; Bioinformatics [q-bio.QM]; Machine Learning [stat.ML]; Applications [stat.AP]

Anne-Claire Haury, Pierre Gestraud, Jean-Philippe Vert. The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures. PLoS ONE, 2011, pp.6(12): e28210. ⟨10.1371/journal.pone.0028210⟩. ⟨hal-00559580v2⟩