title: Performance of variable selection methods using stability-based selection.
abstract: BACKGROUND
  Variable selection is frequently carried out during the analysis of many types of high-dimensional data, including those in metabolomics. This study compared the predictive performance of four variable selection methods using stability-based selection, a new secondary selection method that is implemented in the R package BioMark. Two of these methods were evaluated using the more well-known false discovery rate (FDR) as well.
  
  
  RESULTS
  Simulation studies varied factors relevant to biological data studies, with results based on the median values of 200 partial area under the receiver operating characteristic curve. There was no single top performing method across all factor settings, but the student t test based on stability selection or with FDR adjustment and the variable importance in projection (VIP) scores from partial least squares regression models obtained using a stability-based approach tended to perform well in most settings. Similar results were found with a real spiked-in metabolomics dataset. Group sample size, group effect size, number of significant variables and correlation structure were the most important factors whereas the percentage of significant variables was the least important.
  
  
  CONCLUSIONS
  Researchers can improve prediction scores for their study data by choosing VIP scores based on stability variable selection over the other approaches when the number of variables is small to modest and by increasing the number of samples even moderately. When the number of variables is high and there is block correlation amongst the significant variables (i.e., true biomarkers), the FDR-adjusted student t test performed best. The R package BioMark is an easy-to-use open-source program for variable selection that had excellent performance characteristics for the purposes of this study.
authors.affiliation: Sick Kids Research Institute, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.
authors.forename: Danny
authors.initials: D
authors.lastname: Lu
authors.affiliation: Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, 10-113 Translational Research Center, 3400 Civic Center Blvd, Bldg 421, Philadelphia, PA, 19104, USA.
authors.forename: Aalim
authors.initials: A
authors.lastname: Weljie
authors.affiliation: Department of Mathematics and Statistics, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada. adeleon@ucalgary.ca.
authors.forename: Alexander R
authors.initials: AR
authors.lastname: de Leon
authors.affiliation: Department of Surgery, University of British Columbia, 950 West 10th Avenue, Vancouver, BC, V5Z 1M9, Canada.
authors.forename: Yarrow
authors.initials: Y
authors.lastname: McConnell
authors.affiliation: Department of Oncology, Tom Baker Cancer Centre, University of Calgary, 1331-29th St NW, Calgary, AB, T2N 4N2, Canada.|Department of Surgery, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada.
authors.forename: Oliver F
authors.initials: OF
authors.lastname: Bathe
authors.affiliation: Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, 10-113 Translational Research Center, 3400 Civic Center Blvd, Bldg 421, Philadelphia, PA, 19104, USA. kakopciu@ucalgary.ca.|Department of Oncology, Tom Baker Cancer Centre, University of Calgary, 1331-29th St NW, Calgary, AB, T2N 4N2, Canada. kakopciu@ucalgary.ca.|Cancer Epidemiology and Prevention Research, Alberta Health Services, 2210 - 2 Street SW, Calgary, AB, T2S 3C3, Canada. kakopciu@ucalgary.ca.
authors.forename: Karen
authors.initials: K
authors.lastname: Kopciuk
mesh_terms: D000818:Animals; D015415:Biomarkers / Q000032:analysis*; D003198:Computer Simulation; D006801:Humans; D016018:Least-Squares Analysis; D055432:Metabolomics / Q000706:statistics & numerical data*; D008962:Models, Theoretical; D015999:Multivariate Analysis; D012372:ROC Curve; D015203:Reproducibility of Results; D013223:Statistics as Topic / Q000379:methods*
chemical_list: D015415:Biomarkers
keywords: False discovery rate (FDR); High-dimensional biological data; Partial area under the receiver-operating characteristic curve (pAUC); Stability-based variable selection; Variable importance in projection (VIP)