MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach

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Title: MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach
Authors: Nyamundanda, Gift
Gormley, Isobel Claire
Fan, Yue
Gallagher, William M.
Brennan, Lorraine
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Date: 21-Nov-2013
Online since: 2013-11-29T09:41:08Z
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.
Funding Details: Health Research Board
Irish Research Council for Science, Engineering and Technology
Type of material: Journal Article
Publisher: BioMed Central
Journal: BMC Bioinformatics
Volume: 14
Start page: 338
Keywords: Null distributionSample size estimationPilot dataOptimal sample sizeLoading matrix
DOI: 10.1186/1471-2105-14-338
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection

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