Multivariate analysis and classification of pathogenic priming components in wild-type and lab mice

Abstract: Animal models have a long history of being used in research for the purpose of investigating biological processes and testing the effect of specific compounds on the functionality of biological processes. Different types of mice are used as animal models, most notably inbred and outbred strains. This study investigates the effect of certain priming conditions on the production of cytokines in wild mice and lab mice, using multivariate data analysis. This analytical study involves exploratory analysis, in the form of PCA, MANOVA and LDA, training of different classification models and their validation. Based on the conducted exploratory analysis, certain priming conditions (CD3CD28, CPG and PG) have been identified as clearly defined groups by PCA and LDA, in both wild mice and lab mice. MANOVA concluded that most of the variables tested are statistically significant in determining group association. Subsequent classification modeling determined that the Random Forest algorithm is the most accurate in predicting class, in both the wild and lab mice. The performed analysis has given insight into the major trends exhibited by the data, but further post-processing analysis could potentially extract more data. The results of this study could be used to further investigate the discovered pattern in the data or be supplemented by comparing additional mouse strains under the same experimental conditions.