Similar coefficient values within a principal component indicate that the behavioral indicators exhibited a similar covariation. The sickness indicators

(change in weight between Day 0 and 2, change in weight see more between Day 2 and 5, locomotor activity and rearing) received the most extreme coefficients in principal component 1. The nearly opposite coefficients of both weight changes correspond to the opposite patterns of weight change prior and post Day 2 in BCG-treated mice. On the other hand, the similar coefficients received by the horizontal locomotor activity and rearing are consistent with the similar impact of BCG-treatment on both activity indicators at Day 6. The coefficients in principal components 2 and 3 distinguished sucrose preference Caspase inhibitor review from the other two depression-like indicators of immobility. The results from PCA supplement

those from cluster analysis because meanwhile cluster analysis identifies groups of variables (mice or behavioral indicators) alike (based on indicators or mice, respectively), PCA is a process for identifying combination of the original variables (mice or behavioral indicators) that represent information comparable to the original variables. The outcome from cluster analysis is the grouping of the original variables based on a criterion (e.g. variation between versus within clusters) meanwhile the outcomes from PCA are linear indices of the original variables. The coefficients of the variables in the indices offer insights into the relationship between the original variables and this information is expected to be consistent or complementary to the relationships identified in the cluster analysis. The PCA coefficients received by the behavioral indicators depicted in Fig. 5 are consistent with the clustering of indicators presented in Fig. 3. The pair of indicators locomotor activity and rearing and the check details pair of indicators tail suspension test and sucrose preference test appear closer to each other. The three dimensions of the PCA reported offer additional information

to the one dimension of the lengths of the cluster tree branches. For example, meanwhile the weight change between Day 0 and Day 2 and the weight change between Day 2 and Day 5 received coefficients of similar magnitude for principal components 1 and 3, the magnitudes differ for principal component 2. Another complementary insight from the consideration of three principal components relative to cluster analysis is the characterization of the relationship between the three depression-like indicators. Sucrose preference received coefficients of similar magnitude to tail suspension and forced swim immobility for principal components 1 and 2 and different for principal component 3. This evaluation of the changes in the relationship between the coefficients across principal components further confirms the supplementary information provided by the three dimensions considered.