The article presents two tests for comparing covariance matrices in high-dimensional data sets. One test looks at the overall covariance matrices, while the other focuses on specific parts that show how different segments of the data are related. These tests are useful when dealing with situations where there are many variables but not a lot of data points. Unlike traditional methods, these tests don't require specific assumptions about the data distributions. Overall, these tests can help researchers analyze relationships between different groups of variables, like those related to gene functions.