*4.4. Statistical Analysis*

We conducted the statistical analysis in R computational language [45] and we refined the artwork by GIMP (version 2.10, www.gimp.org). Prior to univariate analysis, we transformed the data to normality by BoxCox transformation [49]. To investigate the e ffects of sex on urinary metabolites, we considered only adult, non-pregnan<sup>t</sup> gira ffes. This allowed us to reduce potential interferences due to di fferent age classes. We then highlighted any di fference by t-test. To investigate age related effects, by removing sex e ffect, we applied a two-way ANOVA test followed by Tukey-HSD, by taking advantage of the "aov" function of the R package "stats" [50]. For the above statistical tests, we accepted a cut-o ff *p*-value of 0.05.

In agreemen<sup>t</sup> with Bazzano et al. [51], we highlighted any trend characterizing the samples with robust principal component analysis (rPCA) models [52], using the molecules accepted by univariate analysis as a base. We took advantage of the PcaHubert algorithm implemented in the "rrcov" package. The algorithm grants robustness with a two-steps approach. In the first step outlying samples are detected according to their distance from the others along and orthogonally to the PCA plane. A second step determines the optimal number of principal components (PCs). The main features of each rPCA model are summarized by a scoreplot and by a Pearson correlation plot. The former is the projection of the samples in the PC space and highlights the underlying structure of the data. The latter relates the concentration of each variable to the components of the model.
