Statistica 12

χ2 and Fisher’s exact test were done for comparing categorical data, using Statistica 12 (StatSoft, Inc. Tulsa, OK) and VassarStats (http://vassarstats.net). Mann–Whitney U test was used for comparing continuous data, using Statistica (Statsoft, Inc.). Kaplan–Meier survival analyses were done using Statistica 12 (Statsoft, Inc.). Significant findings from univariate logistic regression were further explored by multiple logistic regression models, using a forward stepwise approach (Statistica v.13.1, Statsoft, Inc.). Due to the small sample size, subsets of no more than ten genes were analyzed in each multiple logistic regression model. P < 0.05 was considered Statistically significant in all Statistical analyses. A minimum spanning tree (MST) based on the 62 virulence genes present in at least one isolate was constructed using BioNumerics v. 6.1 (Applied Maths, Sint-Martens-Latem, Belgium).

The data representing the effect of the test compounds on ghrelin release from intestinal segments and on plasma ghrelin levels and tissue ghrelin content were assessed for normality of distribution. As the data were distributed in a non-normal and/or non-homogeneous manner, log-transformed data were used to examine the main effects of the compounds using multivariate analysis of variance (MANOVA). An interaction effect between compounds and genotypes was included in the model as well. Post-hoc t-tests with Holm-Sidak correction for multiple testing were applied (SAS Studio University Edition 9.4). Results are presented as predicted values ± standard error of the predicted values.
Dose-response curves of the test compounds in the MGN3-1 cell line are represented as ± standard error of the mean (SEM) and were analyzed using a repeated measures analysis (factors; compound and dose), followed by planned comparisons post-hoc testing and Bonferroni correction (Statistica 12, Statsoft). The effect of the different antagonists on the effect of the test compounds on ghrelin release was analyzed with two-way ANOVA, followed by planned comparisons post-hoc testing and Bonferroni correction (factors; compound and antagonists) (Statistica 12, Statsoft). Significance was accepted at the 5% level.

Statistical analyses, including calculation of the mean value and the standard deviations, were performed with Microsoft Excel software (Microsoft 365). The significance of difference of the mean values between the samples was determined with the program Statistica 12 (Statistica 12, StatSoft Inc., Tulsa, OK, USA). Normality of the data distribution was analyzed using the Shapiro–Wilk’s test, whereas variance homogeneity was verified using Levene’s test. Non-parametric tests (Mann–Whitney U and Kruskal–Wallis) were used to compare the mean values of the features between the samples due to the non-fulfillment of the requirements for normal distribution of data and variance homogeneity. Using both tests, the significance level was 0.05. The assumed grouping factors were the species of mushrooms, as well as the place of their collection. Principal component analysis (PCA) was carried out to show the differences between the studied mushroom species and regions. The results of the experiment and their statistical interpretation are presented in the table and the figure.

Statistical analyses were performed with Statistica 12.0 (StatSoft, Statistica 12.0, Tulsa, Oklahoma, USA) software. Chi-square, Pearson chi-square and Fisher exact tests were used to identify relations between categorical variables. Comparison of numerical variables was performed using one-way ANOVA with post-hoc Tukey test or non-parametric Kruskal-Wallis test depending on the sample size. Allelic separation consistency within observed group of patients with expected allele distribution according to Hardy-Weinberg's rule was checked with use of the Chi-square test. Multiple logistic regression analysis was performed to identify risk factors of increased HD-Mtx therapy toxicities. Bonferroni correction for multiple comparisons was applied when assessing associations of toxicities and genetic variants, separately for each gene assessed. P-value of <0.05 was considered statistically significant.

For the EOs and PEs disc inhibition zone experiment, the means were analyzed by one-way analysis of variance (ANOVA) followed by a Tukey post hoc multiple comparison test using the Statistica 12.0 software package (StatSoft, TIBCO, Palo Alto, CA, USA). Results were expressed as mean (M) ± standard deviation (SD). p values < 0.05 were considered significant.
For the rest of the experiments, including the determination of MIC and MBC, phytotoxicity, and control against an artificial infection on soybean, data analysis was carried out using the analysis of variance method using Statistica 12.0 (StatSoft, TIBCO, Palo Alto, CA, USA), comparing the average values by the criterion Duncan. The percentage data were converted to arcsine before processing. Graphs were created using GraphPad Prism 9.2.0. (GraphPad Software Inc., San Diego, CL, USA).

The data obtained on repetition are represented as mean values with standard deviations. The results were compared among the samples using the analysis of variance (ANOVA) technique, using Statistica 12.0 (StatSoft, Tulsa, OK, USA). The ANOVA indicated differences among the means, and a Tukey analysis (HSD) of the differences was used for comparisons between individual FSM samples. The bivariate correlation between the analytical values obtained for the FSM samples was also evaluated (see Appendix B). Normalized analytical data were used for multivariate statistical evaluation. Hierarchy cluster analysis (HCA; Ward method of clustering, square Euclidean distances) and principal component analysis (PCA; covariation matrix) of the normalized data were carried out. All statistical analyses were performed using Statistica 12.0 software (Statsoft, Tulsa, OK, USA). The graphical outputs, i.e., dendrograms of similarity and component score graphs, were created using Origin 6.0 software (OriginLab, Northampton, MA, USA).

Data were first analysed within each generation. We then compared the data for the three selection lines in S5 against the average values of the same data in P0, and report the results of the post hoc comparisons performed when significant effects were detected. Mean values ± SEM were calculated for all variables. The data were checked for normality using Q-Q plots of residuals, and were in most cases found to be sufficiently normally distributed to allow usage of ANOVA for analysing effects of sex, selection and their interaction. When significant deviations from normality were detected, the data were log transformed. The diagrams all show original data values. The program Statsoft Statistica 12 was used for the analysis.