Abstract
BackgroundDetermining 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.ResultsIn 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.ConclusionsThe 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.
Highlights
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
In many metabolomic experiments, one of the most important objectives is to discover the set of metabolites that plays a significant role in distinguishing samples from two different groups or populations and in the identification of novel biomarkers [1]
In the metabolomics literature, there is no standard method for the determination of sample size when designing a metabolomic experiment
Summary
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. Several methods currently exist in the literature for sample size selection in the high-dimensional data setting [2,3,4,5]. None of these methods are suitable for metabolomic experiments since many either assume variables have equal variance or are independent. These methods rely on the existence of some experimental pilot data on which the actual sample size selection is based, and are not based on the method to be used to analyze the data. Experimental pilot data are rarely available, making such sample size selection approaches redundant
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