Univariate and multivariate analysis for metabolomic
profiling
After high-throughput screening of the metabolic features using the
above rules, a total of 772 (473 from positive ion mode and 299 from
negative ion mode). For each metabolite peak reproducibly detected in
whole samples,
the
unpaired two-tailed Wilcoxon rank-sum test (a nonparametric univariate
method) was carried out to measure the significance of each metabolite
among different groups and adjusted by false discovery rate (FDR)
correction (Benjamini–Hochberg method). The merged UHPLC-MS data were
log-transformed for subsequent analyses. The filtered and normalized
data matrix was exported for multivariate statistical analysis using
principal component analysis (PCA) and orthogonal partial least-squares
discriminant analysis (OPLS-DA) with SIMCA 16.1 software package
(Umetrics, Umea, Sweden). To further validate the model, the permutation
tests (n = 200) was proceeded. By combining the univariate and
multivariate statistical analysis, on the basis of
a
variable importance in the projection (VIP) threshold of 1 from the
10-fold cross-validated OPLS-DA
model,
fold change < 0.83 or > 1.2, FDR <
0.05, numbers of different metabolites among different groups were
obtained.