Simca p software v13

SIMCA-P + software (V13.0, Umetrics AB, Umea, Sweden) was used for the multivariate analysis after data normalization and pattern recognition, and principal components analysis (PCA) was performed using the Ctr-formatted (mean-centered scaling) data scale conversion method.^{2 (link)} Automatic data modeling and analysis were conducted, and partial least squares-discriminant analysis (PLS-DA) was used to identify the correlation between the data (X variable) and other factors (Y variables, grouping information) using the same software. Partial least squares-discriminant analysis using the Ctr-formatted data scale conversion method was then performed to evaluate the models for the first and second principal components (PCs). The quality of PLS-DA on the model was tested using leave-one-out cross-validation. After cross-validation, R²X (explainable index) and Q² (predictable index) values were obtained to judge the validity of the model. The validity of the model was further tested by arranging several randomized experiments (n = 200) to obtain corresponding values of different random Q² values by changing the order of the categorical variables.^{3 (link)}

To normalize the metabolites for data analyses, a data normalization step was performed by registering the median level of each compound to equal to one (1.00). And meanwhile, the missing values (if any) were assumed to be below the limits of detection and were imputed with the observed minimum values. Log transformation of normalized data, ANOVA contrasts, Welch's two-sample t-test and paired t-test were used to identify biochemical which were significantly different between before and post chemotherapy in RAML patients.^{31 (link)}P values less than 0.05 was defined as statistical significance. SPSS 17.0 (IBM, New York, US) and MultiExperiment Viewer 4.8 software packages were used for data analysis.^{37 (link)} The metabolic data was visualized by K-Medians clustering method and stoichiometry including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), and SPSS scatter dot plots. SIMCA-P software (v13.0, Umetrics, Malmö, Sweden) was used in this study.