<p>To illustrate differences in methylation status between histological GCT subtypes two (visualization) methods were applied. Firstly, the methylation pattern over the whole genome and specific functional categories (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g001" target="_blank">Fig 1C</a>) is visualized using the distribution of the methylation percentage β in all samples of a certain GCT subtype. Next, the discriminatory power of the methylation pattern for each individual sample is shown using principal component analysis. <b>(A) Distribution of methylation percentage.</b> Violin plots: grey areas indicate a kernel density plot of the methylation percentage (β) of all probes in all samples in a certain category. The boxplot indicates the interquartile range (black bars) and median (white squares). X-axis labels indicate histological subgroup according to Fig <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g001" target="_blank">1A</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g001" target="_blank">1B</a>. TE indicates type I TE only. <b>(B) Principal Component Analysis.</b> The first two principal components (PC) are plotted to evaluate the discriminative power of the methylation pattern between the subtypes. Abbreviations of histological subtypes are explained in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g001" target="_blank">Fig 1A</a>. CL indicates cell lines. Please note that in the legend of the PCA the TE group is subdivided based on gender and localization: I = type I; II = type II/formally part of the mNS group, s = sacrum, t = testis, o = ovary, m = male, f = female. A more detailed visualization of the TE classes is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.s002" target="_blank">S2 Fig</a>, which also includes the full series of 18 functional categories, bootstrap validation of the PCA and an estimation of the variance explained by the first two principal components.</p
