Extensions of stability selection using subsamples of observations and covariates

We introduce extensions of stability selection, a method to stabilise variable selection methods introduced by Meinshausen and B\"uhlmann (J R Stat Soc 72:417-473, 2010). We propose to apply a base selection method repeatedly to random observation subsamples and covariate subsets under scrutiny, and to select covariates based on their selection frequency. We analyse the effects and benefits of these extensions. Our analysis generalizes the theoretical results of Meinshausen and B\"uhlmann (J R Stat Soc 72:417-473, 2010) from the case of half-samples to subsamples of arbitrary size. We study, in a theoretical manner, the effect of taking random covariate subsets using a simplified score model. Finally we validate these extensions on numerical experiments on both synthetic and real datasets, and compare the obtained results in detail to the original stability selection method.Comment: accepted for publication in Statistics and Computin

Impact of biological matrix on inflammatory protein biomarker quantification based on targeted mass spectrometry

International audienceBACKGROUND: Serum and plasma are widely used matrices in biological and clinical studies. To improve reliability and consistency of markers quantification, the influence of these matrices on proteins was evaluated by targeted mass spectrometry. RESULTS: 65 proteins were quantified in matched blood samples collected in serum, ethylenediaminetetraacetic acid and heparin plasma tubes from 40 healthy and 10 pathological individuals. Only 52% of the proteins were not impacted by any of the biological matrices tested, and the effects on quantification of proteins affected was matrix and protein dependent. CONCLUSION: Matrix comparisons using mass spectrometry is therefore recommended to assess the relevance of using surrogate matrix, performing biomarker discovery study or evaluating the clinical use of biomarkers in large clinical cohorts