Exploratory analysis of immunological responses in wild and laboratory mice

Sammanfattning: The use of mice as a model organism in biomedical research is widespread due to their similarities with humans in anatomy and physiology. However, the genetic isolation of inbred strains from natural populations raises concerns about the reliability of these models. Studies have shown differences in immune responses between wild and laboratory mice, and the use of exploratory tools for large datasets can help to analyze and interpret these differences. The aim statistical analysis of dataset can provide insights into the differences between variables and individuals and guide further investigations. The results showed that LDA provided clear separation between the different groups, and successfully differentiated between stimulation types and mouse strains, with distinct clustering of data points. The KNN algorithm performed best for smaller values of K. However, the selected gender characteristic did not possess strong discriminatory power in separation and further investigation into alternative features or methodologies may be necessary. In conclusion, the aim of providing comparative immunological analysis of wild and laboratory mice types is achieved. This study underscores the importance of careful statistical analysis, acknowledges the limitations of imputation methods, and highlights the potential of LDA and KNN algorithms in analyzing immune response data. As well, highlighting the need for improved models that are able to capture the complexities of immune responses and their relevance to human immunology.