Computational protocol: 1H NMR-Based Metabonomic Study of Functional Dyspepsia in Stressed Rats Treated with Chinese Medicine Weikangning

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Protocol publication

[…] The results were imported into SIMCA-P + 12.0 (Umetrics, Sweden) for multivariate statistical analysis. An unsupervised principal component analysis (PCA) model was firstly used to identify general trends and outliers through a mean-centered approach and the plotting of principal component (PC) score plots. To minimize biological analytical variation and improve the separation between groups, partial least-squares projection to latent structures-discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) were performed for model analysis in a unit variance-scaled approach. R2 and Q2 values were used to assess the amount of variation represented by PC and to verify the robustness of the models, respectively, and the models were cross-validated by permutation tests (permutation numbers 200) [, ]. Variable importance in projection (VIP) values and correlation coefficients (Pcorr) were both used to select the metabolites with statistically significant differences. In this study the |Pcorr| values > 0.60207 (for degree of freedom = 9) in the modeling stage or > 0.63190 (for degree of freedom = 8) in drug intervention and VIP values > 1 were a priori considered as the cutoff value according to the literature []. Besides, Chenomx NMR Suite 7.51 software (Chenomx Inc., Canada) was adopted in the work of assignments of metabolites []. In addition, Independent-Samples t-test (including t′-test) and Mann–Whitney U test were used to detect significant differences in selected signals between every two groups using SPSS Statistics Base 18.0 (SPSS Inc., USA), and P values of less than 0.05 were considered to represent significant differences. […]

Pipeline specifications

Software tools Chenomx NMR Suite, SPSS
Applications Miscellaneous, NMR-based metabolomics
Organisms Rattus norvegicus