Figure 2

Results of a principal component analysis (PCA). Projection of the 80 × 7 data matrix obtained by averaging the ratings of perceptual odor properties, separately for each of the seven properties, the 40 odors and the 2 olfactory diagnoses. (A) Plot of the data projected on the space given by the first two principal components (Dim.1 versus Dim.2). The PCA plot shows the separation of olfactory diagnoses mainly to the right in Dim.1 and to the top in Dim2, see the thick arrow indicating the averages of the PCA coordinates between olfactory diagnoses. The same odors rated by either normosmic or hyposmic subjects are connected with arrows (paired data). (B,C) The marginal distribution plots show the segregation of the pain phenotype groups along the principal components. (D) Plots the Eigenvectors of a variable in PCA Dim1 versus Dim2. (C) Scree-plot of the amount of variance of the data captured by each principal component. (E) Bar graph of the explained variance by each principal component. (F) Bar graph of the contribution of each perceptual property to Dim.1. The dashed horizontal reference dashed corresponds to the expected value if the contribution where uniform. (G) Bar graph of the contribution of each perceptual property to Dim.2. (H) Sorted Euclidean distances between the same odors evaluated by either normosmic or hyposmic subjects, i.e., the lengths of the arrows in panel A. The vertical dotted lines show the decision boundaries obtained by ABC analysis of the distances. The figure has been created using the R software package (version 4.0.3 for Linux; https://CRAN.R-project.org/ (R Development Core Team, 2008)) and the libraries “ggplot2” (https://cran.r-project.org/package=ggplot2 (Wickham, 2009)) and “FactoMineR" (https://cran.r-project.org/package=FactoMineR⁶⁹).