I am currently struggling to find the most proper test to serve my needs. The thing is, I have one data matrix. The columns are computational features and each row corresponds to a patient. The last column of the matrix is the label of the patient, which is called 'Outcome'. There are two categories: R (Responder to immuno-therapy) and NR (Non-responder to therapy). Now, I want to select the most informative features and then input to the support vector machine for patient classification. To achieve this goal, I did the followings:
First, for each feature, I split the data to NR-associated values and R-associated values, then I would like to perform a 'test' for these two vectors to see if there is a significant difference. If yes, the feature is maintained, if no, the feature is discarded.
Second, since there are thousands of features, therefore I need to perform the test for thousands of times. So after I calculated the p value, I also performed a Bonferroni correction to obtain adjusted p value.
The adjusted p values are used for feature screening.
Initially, I used Student's t-test. However, I am struggling if this is the appropriate test to fit the scenario here, since:
- There are only ~150 patients. The number of samples is not that high.
- The length of Responder's vector and Non-Responder's vector are not the same. For example, there are ~100 responders but only ~50 non-responders.
Can anyone please give me some suggestions and explain the rationale? Thank you!