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

Hdl Handle:
http://hdl.handle.net/10147/315480
Title:
MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach.
Authors:
Nyamundanda, Gift; Gormley, Isobel C; Fan, Yue; Gallagher, William M; Brennan, Lorraine
Citation:
BMC bioinformatics. 2013 Nov 21;14(1):338
Journal:
BMC bioinformatics
Issue Date:
21-Nov-2013
URI:
http://dx.doi.org/10.1186/1471-2105-14-338; http://hdl.handle.net/10147/315480
Abstract:
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.
Item Type:
Article
Language:
en
Keywords:
PHYSIOLOGY
Local subject classification:
METABOLISM

Full metadata record

DC FieldValue Language
dc.contributor.authorNyamundanda, Giften_GB
dc.contributor.authorGormley, Isobel Cen_GB
dc.contributor.authorFan, Yueen_GB
dc.contributor.authorGallagher, William Men_GB
dc.contributor.authorBrennan, Lorraineen_GB
dc.date.accessioned2014-04-07T11:34:50Z-
dc.date.available2014-04-07T11:34:50Z-
dc.date.issued2013-11-21-
dc.identifier.citationBMC bioinformatics. 2013 Nov 21;14(1):338en_GB
dc.identifier.urihttp://dx.doi.org/10.1186/1471-2105-14-338-
dc.identifier.urihttp://hdl.handle.net/10147/315480-
dc.description.abstractAbstract 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.-
dc.language.isoenen
dc.subjectPHYSIOLOGYen_GB
dc.subject.otherMETABOLISMen_GB
dc.titleMetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach.en_GB
dc.typeArticleen
dc.identifier.journalBMC bioinformaticsen_GB
dc.language.rfc3066en-
dc.rights.holderGift Nyamundanda et al.; licensee BioMed Central Ltd.-
dc.description.statusPeer Reviewed-
dc.date.updated2014-04-05T11:17:07Z-
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