A Data Science Perspective for the Effective Selective Breeding Process to Produce Striking Behavioral Differences Between Two Rat Substrains During Several Generations

Development of reliable rodent models for neuropsychiatric diseases (e.g. depression, Alzheimer disease, Parkinson disease or schizophrenia) is very difficult due to the high complexity of the central nervous system. This study analyzed data from more than 1,100 male and female rats spanning 14 generations originated from Long Evans rats to develop its special substrain (Lisket) with schizophrenialike phenomenon. Data analysis focused on the development of differences between the two groups in several behavioral measures. Key methodologies included data normalization, t-tests, Principal Component Analysis (PCA) and Agglomerative Clustering to uncover group-specific behavioral patterns. These analyses revealed that the statistically significant changes (e.g. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p&lt;0.001$</tex>) developed gradually in the new substrain both in males and females. The findings highlight the utility of combining traditional behavioral measurement techniques with modern data science tools to explore the development of complex behavioral alterations during several generations in the newly developed substrain. The applied complex data analytic method might be a reliable technic for selecting animals with appropriate characteristics to produce the new generations with even more proper features of the planned model. This research may contribute to the development of new models of different psychiatric conditions.