  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.
