Jmp pro software

The statistical analyses of microbiota data and the correlation analyses were performed using the JMP Pro software (version 13, SAS Institute Inc.). Normality of the data was tested using the Shapiro-Wilk test^{19 (link)}. Based on the normality of the data, culturing data were compared between the dietary treatments using student’s t-test corrected with false discovery rate (FDR); while the alpha diversity indices were analyzed using the non-parametric Kruskal–Wallis test with Dunn’s multiple comparison post-hoc test. The OTU table was filtered prior to the differential abundance analysis for the bacterial taxa, such that only the OTUs that were observed at least 100 times among all the samples and in at least 15 samples were kept for the analysis. The differentially abundant taxa were characterized using the linear discrimination analysis effect size (LEfSe) algorithm online Galaxy version, with parameters of α = 0.05 and LDA score 2.0. Principle coordinate analysis (PCoA) and permutational multivariate analysis of variance (PERMANOVA) on the unweighted and weighted UniFrac distance metrics were performed using the web-based tool, MicrobiomeAnalyst²⁰ .

This study was focused on studying two factors, the environments and genotypes. This is why the experiments were laid out in the field in a split-plot design with three replications where environments (treatments) were randomly assigned as main plots and genotypes were randomized as subplots within the main plots. The means and standard deviation for each trait of all studied genotypes were calculated.
Furthermore, the means for treatments, genotypes, and their interaction were compared to mark any significance by using the LSD test at the 5% level of probability.
The reduction percentage (R%) of each trait under SE, relative to the measurement under NE, was estimated per season using the equation number (2) according to [19 ]: where Y and Y_P are as defined above for HSI.
Pearson correlation coefficient (r) was used to study the pairwise relationship between measured traits. Two multivariate approaches were performed, including the Principle Component Analysis (PCA) and the two-way hierarchical clustering, using JMP Pro software (version 8.0; SAS Institute, Cary, NC, USA). The PCA was used to demonstrate the dimension of relationships among the measured traits. While the two-way hierarchical clustering helped to group the genotypes in association with the phenotype traits using the average linkage method. The clusters were coloured based on the K-means clustering approach.

All results are shown as means ± standard deviation (SD) or standard error. The significance of differences between groups was assessed by one-way analysis of variance (ANOVA) and t tests. The diagnostic metrics of accuracy, sensitivity, and specificity at a diagnostic cutoff of WR 10%, and the area under the receiver-operating characteristic (ROC) curve (AUC) were calculated. Multiple regression fit models between methods were evaluated with correlations for comparison. A P value < 0.05 was considered statistically significant. All the statistical analyses were performed by JMP Pro software (version 16.0.0, SAS Institute, Cary, NC, USA).