title: MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach.
abstract: BACKGROUND
  Determining sample sizes for metabolomic experiments is important but due to the complexity of these experiments, there are currently no standard methods for sample size estimation in metabolomics. Since pilot studies are rarely done in metabolomics, currently existing sample size estimation approaches which rely on pilot data can not be applied.
  
  
  RESULTS
  In this article, an analysis based approach called MetSizeR is developed to estimate sample size for metabolomic experiments even when experimental pilot data are not available. The key motivation for MetSizeR is that it considers the type of analysis the researcher intends to use for data analysis when estimating sample size. MetSizeR uses information about the data analysis technique and prior expert knowledge of the metabolomic experiment to simulate pilot data from a statistical model. Permutation based techniques are then applied to the simulated pilot data to estimate the required sample size.
  
  
  CONCLUSIONS
  The MetSizeR methodology, and a publicly available software package which implements the approach, are illustrated through real metabolomic applications. Sample size estimates, informed by the intended statistical analysis technique, and the associated uncertainty are provided.
authors.affiliation: School of Mathematical Sciences, University College Dublin, Dublin, Ireland. claire.gormley@ucd.ie.
authors.forename: Gift
authors.initials: G
authors.lastname: Nyamundanda
authors.forename: Isobel Claire
authors.initials: IC
authors.lastname: Gormley
authors.forename: Yue
authors.initials: Y
authors.lastname: Fan
authors.forename: William M
authors.initials: WM
authors.lastname: Gallagher
authors.forename: Lorraine
authors.initials: L
authors.lastname: Brennan
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D008137:Longitudinal Studies; D055432:Metabolomics / Q000706:statistics & numerical data*; D015233:Models, Statistical; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods; D010865:Pilot Projects; D025341:Principal Component Analysis / Q000592:standards; D018401:Sample Size; D012984:Software