Fig. 3

Result of variance analysis performed on proteomic data. a Individual sample map related to principal component analysis (PCA) of spot normalized intensities related to 19 spots. Sample names indicate location (IS = Isernia; AL = Alba; SM = San Miniato) and year of sample collection. Data reported represents an average value for each year of analysis. F1 = first dimension, F2 = second dimension. Total inertia (i.e., total variance) included by the first two dimensions of PCA accounted for 72.21% of the variance. b Correlation circle (variables factor map) related to the contribution of each variable (spot) in the distribution of the observations (samples). The length and the direction of the vectors are directly correlated to their significance. The angle between two vectors (α) defines the correlation of the associated variables: Positive correlation is present if 0 < α < 90°, while the correlation is negative if 90 < α < − 180°. No linear dependence exists if α = 90°. c Heat map based on quantitative data related to normalized spot intensities, whose discrete color scale is shown in the box. Green indicates over-representation, red down- representation. d Results of aggregative hierarchical clustering (AHC) analysis performed on spot data. C1-C3, sample distribution classes, based on their dissimilarity coefficient. The dotted line represents the degree of truncation of the dendrogram, used for creating classes and automatically chosen by the entropy level. Sample names correspond to those reported in Table 1