Power Analysis and Sample Size Determination in Metabolic Phenotyping.
Estimation of statistical power and sample size is a key aspect of experimental design. However, in metabolic phenotyping, there is currently no accepted approach for these tasks, in large part due to the unknown nature of the expected effect. In such hypothesis free science, neither the number or class of important analytes nor the effect size are known a priori. We introduce a new approach, based on multivariate simulation, which deals effectively with the highly correlated structure and high-dimensionality of metabolic phenotyping data. First, a large data set is simulated based on the characteristics of a pilot study investigating a given biomedical issue. An effect of a given size, corresponding either to a discrete (classification) or continuous (regression) outcome is then added. Different sample sizes are modeled by randomly selecting data sets of various sizes from the simulated data. We investigate different methods for effect detection, including univariate and multivariate techniques. Our framework allows us to investigate the complex relationship between sample size, power, and effect size for real multivariate data sets. For instance, we demonstrate for an example pilot data set that certain features achieve a power of 0.8 for a sample size of 20 samples or that a cross-validated predictivity QY(2) of 0.8 is reached with an effect size of 0.2 and 200 samples. We exemplify the approach for both nuclear magnetic resonance and liquid chromatography-mass spectrometry data from humans and the model organism C. elegans.
Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London , London SW7 2AZ, U.K.|Hospices Civils de Lyon, Service de Réanimation Néonatale et Néonatalogie, Hôpital Femme Mère Enfant , 59 bd Pinel, 69677 Bron Cedex, France.
Benjamin J
BJ
Blaise
Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London , London SW7 2AZ, U.K.
Gonçalo
G
Correia
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health , 615 North Wolfe Street, Baltimore, Maryland 21205, United States.
Adrienne
A
Tin
Johns Hopkins Bloomberg School of Public Health, Department of Medicine, The Johns Hopkins University and The Welch Center for Epidemiology and Clinical Research , 2024 East Monument Street, Baltimore, Maryland 21205, United States.
J Hunter
JH
Young
Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London , St. Mary's Campus, Norfolk Place, W2 1PG London, United Kingdom.
Anne-Claire
AC
Vergnaud
Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London , London SW7 2AZ, U.K.|MRC-NIHR National Phenome Centre, Department of Surgery and Cancer, Imperial College London , IRDB Building, Du Cane Road, London W12 0NN, U.K.
Matthew
M
Lewis
Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London , London SW7 2AZ, U.K.|MRC-NIHR National Phenome Centre, Department of Surgery and Cancer, Imperial College London , IRDB Building, Du Cane Road, London W12 0NN, U.K.
Jake T M
JT
Pearce
Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London , St. Mary's Campus, Norfolk Place, W2 1PG London, United Kingdom.
Paul
P
Elliott
Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London , London SW7 2AZ, U.K.
Jeremy K
JK
Nicholson
Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London , London SW7 2AZ, U.K.
Elaine
E
Holmes
Biomolecular Medicine, Division of Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London , London SW7 2AZ, U.K.
Timothy M D
TM
Ebbels
D000818:Animals; D017173:Caenorhabditis elegans; D066264:Datasets as Topic / Q000706:statistics & numerical data; D006801:Humans; D055442:Metabolome; D055432:Metabolomics / Q000706:statistics & numerical data*; D015233:Models, Statistical; D015999:Multivariate Analysis; D000074286:Preliminary Data; D018401:Sample Size