Lessons learned from metabolomics in cystic fibrosis

Molecular and Cellular Pediatrics

Table 1 Common statistical approaches used in metabolomics data analysis

	Method	Purpose	Statistical assumptions^a
A. Methods that analyze each metabolite separately
Parametric methods	Paired t test	Compare two groups	Random sampling, normality, paired samples, no major outliers
	Student t test	Compare two groups	Random sampling, normality, independent samples, equal variances, no major outliers
	Welch t test	Compare two groups	Random sampling, normality, independent samples, unequal variances, no major outliers
	Linear model	Compare two or more groups and with the possibility to control confounders	Random sampling, linearity, and additivity, errors are independent, homoscedastic, and follow normal distribution, no major outliers
Nonparametric methods	Wilcoxon signed rank test	Compare two groups	Random sampling, paired samples, differences between paired samples have symmetrical distribution
	Mann-Whitney U test	Compare two groups	Random sampling, independent samples
	Kruskal-Wallis ANOVA	Compare more than two groups	Random sampling, independent samples
B. Methods that analyze all of the metabolites simultaneously
Unsupervised classification methods	PCA	Detect major pattern in the data, detect outliers	Linearity
Supervised classification methods	PLS-DA	Find metabolites that best separate two or more study groups	Linearity, no major outliers
	OPLS-DA	Find metabolites that best separate two or more study groups, with easier result interpretation than PLS-DA	Linearity, no major outliers

^aThe assumption of continuous data is not listed, because all of the metabolomics data are continuous data and meet this assumption