%0 Figure %A Langgartner, Dominik %A M. Füchsl, Andrea %A Kaiser, Lisa M. %A Meier, Tatjana %A Foertsch, Sandra %A Buske, Christian %A O. Reber, Stefan %A Mulaw, Medhanie A. %D 2018 %T Biomarkers for classification and class prediction of stress in a murine model of chronic subordination stress - Fig 1 %U https://plos.figshare.com/articles/figure/Biomarkers_for_classification_and_class_prediction_of_stress_in_a_murine_model_of_chronic_subordination_stress_-_Fig_1/7050233 %R 10.1371/journal.pone.0202471.g001 %2 https://plos.figshare.com/ndownloader/files/12963011 %K body weight gain %K tsp %K SVM %K non-stressed mice %K subordination stress Selye %K PCA %K CSC %K SHC %K triad %K support vector machines %K analysis %K GC %X

(a) A 3D plot showing the result of the Principal component analysis (PCA) using all the 28 physiological and immunological parameters of non-stressed (SHC or No Stress; red) and stressed mice (CSC or Stress). The first three components shown here cumulatively explain 70.5% of the variation in the dataset, segregating the SHC and CSC mice. (b) A bi-plot showing the PCA analysis results (as shown in Fig 1a) with additional information on loadings for each variable in the analysis. The direction and length of arrows indicate the sign and magnitude of the coefficient of each variable in the PC1 and PC2 coordinate, respectively. Size of the balls indicates PC3 coefficient loadings in scales 1 to 5 (shown the right side of the plot). Light red shades and red balls represent SHC samples while purple shades and light blue balls indicate CSC mice. The corresponding amount of variation explained by each component is given in brackets. Details of the numeric labels for each arrow are given in S3 Table.

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