Simca v13

The primary outcomes for the analysis were MA as categorical, and ACR as continuous
variable. Due to different ACR cut-off values males and females were evaluated
separately. Data not fitting to normal distribution were logarithmically transformed
prior to statistical evaluation, but for better understanding means±SD are
presented, if not indicated differently. Multivariate analyses—Principal
component analysis (PCA) and Orthogonal projections to latent structures discriminant
analysis (OPLS-DA) were performed to identify variables contributing to between group
separation (e.g. NA vs. MA), using Simca v.13 software (Umetrics, Umea, Sweden).
Variables with Variable of importance for the projection (VIP) values >1 were
considered important contributors to between group separation, those with VIP
<0.5 unimportant. VIP ranging between 0.5-to-1 is referred to as a
“grey interval”, importance of the variable depends on the sample size.
Two-sided Student's T-test was used to compare 2 groups—NA vs. MA, lower vs.
upper quartile within NA range, lean vs. centrally obese. Categorical data were
compared using chi-square test (with Yates’s correction, if appropriate). SPSS
statistical software (v.16 for Windows, SPSS, Chicago, Illinois) was used, with a
significance set at p<0.05.

Data not distributed normally were logarithmically transformed for statistical analyses. Descriptive statistics are presented as percentages or means ± SD. Two sets of data were compared using two-sided Student's t-test, for comparison of ≥3 sets of data analysis of variance (ANOVA) with post hoc Scheffe's test was employed. Proportions were compared using chi-square test. Pearson's correlation coefficients were calculated. Multivariate analysis was performed using the General Linear Model (GLM). SPSS statistical software (v. 16.0 for Windows; SPSS, Chicago, Illinois) was used with the significance set at P < 0.05. The orthogonal projections to latent structures discriminant analysis (OPLS-DA, Simca v.13 software, Umetrics, Umea, Sweden) was used to identify independent variables contributing to separation between subjects with 25(OH)D deficiency and those with sufficient levels.

The quantitative analysis of the ¹H-NMR spectra was performed on 4–6 samples of MCF-7 (n = 4), MCF-7/TAM (n = 4), MCF-7/shCK-α (n = 6) and MCF-7/TAM/shCK-α (n = 5) cells. Given the number of cell groups and ¹H-NMR-detectable metabolites and the potential correlations among the metabolites, a multivariate analysis was performed using SIMCA (v.13; Umetrics Inc., San Jose, CA). Data were first inspected by performing principal components analysis (PCA), followed by partial least-squares discriminant analysis (PLS-DA). Then, a set of ¹H-NMR measures were sorted out according to the variable influence on projection (VIP) values as a measure of the relative discriminatory potential of the individual ¹H-NMR measures [21 (link)]. Those ¹H-NMR measures with VIP>1 were considered to have contributed most to the differentiation of the cell groups, for which further statistical analyses were performed for multiple group comparisons using the Student’s t-test and analysis of variance (ANOVA). A p value of <0.05 was considered to be statistically significant.