Simca p software version 12

To create a data matrix for multivariate data modelling (PCA), UPLC-MS raw data was exported and analysed using Markerlynx XS software (Waters, MA, USA). The analyses were carried out using different parameters and for maximum data output. The following parameters were chosen: retention time (Rt) of 1–27 min, mass range of 100–1000 Da, mass tolerance of 0.02 Da, and Rt window of 0.2 min. The analyses conditions/parameters were kept constant for both negative and positive data. The final data matrix was imported to SIMCA-P software version 12.0 (Umetrics, Sweden) in order to perform PCA. Unless stated otherwise, all PCA models were pareto scaled. From the PCA loadings plot, metabolites of which the levels were affected by temperature during extraction were selected. Using the m/z of these metabolites, their respective extracted ion chromatograms were generated. The molecular formulae of these ions were computed and selected on the basis that they are within a 5 ppm mass accuracy range. The identities of the ions were searched using the Dictionary of Natural Products database.

Multivariate statistical analyses of NMR data were performed with Pareto scaling using SIMCA-P+ software, version 12.0 (Umetrics, Umeå, Sweden). All changes in metabolite levels, including isoleucine, leucine, valine, AMP, ADP, ATP, lactate, fumarate, and malate, were assessed by Student’s t-test using GraphPad Prism (version 5 for Windows; GraphPad Software). Data are presented as mean ± standard deviation (SD) of individual experiments. All experiments were performed with at least three independent replicates. The difference between mean values was considered statistically significant at p < 0.05.

Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were carried out using SIMCA-P+ software version 12.0 (Umetrics, Umeå, Sweden) to visualize score plots and identify differences between the young and old groups. The Wilcoxon rank sum test or t-test was used to evaluate significant differences in metabolite or RNA expression levels between the young and old groups. Intragroup metabolite level differences in a pathway were evaluated by using 2-way ANOVA with Sidak’s multiple comparisons test implemented in GraphPad. R 1.2.1335 software and relevant packages were utilized for the statistical analyses and figure generation in this research [45 ]. Gene ontology analysis was conducted by using the goseq R package [52 (link)].

Multivariate statistical analyses were performed with unit variance scale by using SIMCA-P+ software, version 12.0 (Umetrics, Umea, Sweden). The Statistical Package for Social Sciences software, version 15.0 (SPSS Inc., Chicago, IL, USA), R studio, version 1.1.453, and GraphPad Prism, version 7.0a (GraphPad Software, Inc., La Jolla, CA, USA) were used to assess statistical significance using the Wilcoxon Signed-Rank test, Mann-Whitney U-test, Spearman’s correlation analysis.

Data analyses of three replicates were performed for every sample. ANOVA was applied with a factorial design (MSTAT-C, software developed by the Crop and Soil Sciences Department of Michigan State University of the United States). Mean separation was tested by application of Tukey’s test. To investigate chemical variation in the 15 different accessions of Bituminaria based on GC and GC/MS, we submitted the data to multivariate statistical evaluation (PCA). PCA is an unsupervised pattern recognition technique that converts data consisting of many interrelated variables to a new coordinate system, thereby reducing dimensionality while maintaining the variance [52 (link)]. PCA reveals trends in a dataset such as groupings and clusters based on chemical similarities or differences, while outliers within the dataset are also identified. The results of PCA were observed in a score scatter plot, displaying the spatial distribution of observations. Prior to chemometric analysis, the total integral areas were set to 100 to normalize the data, and the generated ASCII file was imported into Microsoft Excel for labeling. The matrix was then imported into SIMCA-P software version 12.0 (Umetrics AB, Umea, Sweden) for statistical analysis.