Imperial College London

Emeritus ProfessorJeremyNicholson

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Emeritus Professor of Biological Chemistry



+44 (0)20 7594 3195j.nicholson Website




Ms Wendy Torto +44 (0)20 7594 3225




Office no. 665Sir Alexander Fleming BuildingSouth Kensington Campus






BibTex format

author = {Blaise, B and Correia, G and Tin, A and Young, J and Vergnaud, A and Lewis, M and Pearce, J and Elliott, P and Nicholson, J and Holmes, E and Ebbels, TMD},
doi = {10.1021/acs.analchem.6b00188},
journal = {Analytical Chemistry},
pages = {5179--5188},
title = {A novel method for power analysis and sample size determination in metabolic phenotyping},
url = {},
volume = {88},
year = {2016}

RIS format (EndNote, RefMan)

AB - 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 QY2 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.
AU - Blaise,B
AU - Correia,G
AU - Tin,A
AU - Young,J
AU - Vergnaud,A
AU - Lewis,M
AU - Pearce,J
AU - Elliott,P
AU - Nicholson,J
AU - Holmes,E
AU - Ebbels,TMD
DO - 10.1021/acs.analchem.6b00188
EP - 5188
PY - 2016///
SN - 1520-6882
SP - 5179
TI - A novel method for power analysis and sample size determination in metabolic phenotyping
T2 - Analytical Chemistry
UR -
UR -
VL - 88
ER -