A principal component analysis (PCA) model was used to explore clustering patterns of observations, trends in the data, and outliers. An orthogonal projections to latent structures (OPLS) model was used to evaluate the impact of known metadata on the metabolites and models. Separation of classes and variables related to separation in the data according to classification of diet (vegan compared with nonvegan, meat compared with nonmeat) was evaluated using OPLS with discriminant analysis (OPLS-DA). Receiver operating curve analysis was performed and the AUC was used as an estimate of the predictive accuracy of each dietary group in the OPLS-DA model. To select class-discriminating variables of interest for annotation, loadings (pq ≥ ± 0.1) and top-ranked variables in variable importance scores in the OPLS-DA model were assessed. All multivariate analyses were performed using SIMCA software version 15.0 (Umetrics AB).
Simca software version 15
Manufactured by Sartorius
Sourced in Sweden
SIMCA software version 15.0 is a data analysis and multivariate modeling tool developed by Sartorius. The software's core function is to provide users with advanced statistical techniques for analyzing complex data sets and building predictive models.
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Lab products found in correlation
2 protocols using simca software version 15
1
Metabolomic Analysis of Dietary Patterns
2
Comparative Metabolomics Analysis Protocol
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The data of metabolites presented in this study were the mean values of six biological replicates. The GC-TOF-MS data were processed with SIMCA software (Version 15.0, Umetrics, Umea, Sweden) to obtain the result of multivariate statistical analysis. Principal component analysis (PCA) was performed to compare the DEM level between both samples. Then, DEMs were identified and confirmed between the two samples using the orthogonal partial least square discriminant analysis (OPLS–DA) with the threshold value of variable importance in projection (VIP) >1 and P < 0.05. Subsequently, the expression patterns of DEMs were demonstrated by hierarchical clustering analysis (HCA) and heat maps. The physiological data and qRT-PCR analysis were the mean values of three replicates. A two-way ANOVA was performed. The Fisher test (P < 0.05) was used for mean comparisons between the two samples using SPSS 22.0. The different letters between means were indicative of significant difference at a significant level of P < 0.05.
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