Untargeted metabolic profiles discriminate between different
inflammatory asthma phenotypes and healthy subjects in the discovery
set
The score plot of OPLS-DA analysis showed a distinct separation between
different asthma phenotypes and the Asthma group was also clearly
separated from the Healthy group (Figure 1). The parameters for the
classification models from the software were showed in the Table E3,
suggesting all of the model had a very good fitness and predictive
capability. Furthermore, a leave-one-out cross-validation (LOOCV) was
used to estimate the robustness and predictive ability of each model,
and thus a 200 permutation test was applied. The low value of the
Q2 intercept in each model indicated the robustness of
these model and thus showed a low risk of over-ļ¬tting in all of the
comparison.
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, a total of
77 differential metabolites were screened between different asthma
phenotypes and healthy subjects as shown in the heatmap in the Figure 2.