Am I using Student's t-test correctly for feature selection? I have a $198$-sample dataset containing miRNA types (numerical features) and one categorical feature "Type" with values "Tumor" or "Healthy". 
  Index     miRNA1          miRNA2          miRNA3            Type
   1       48421.52        24242.14        23842.1518        Tumor
   2       2757.96         28965.2         7339.57           Healthy
   3       4300.34         52565.07        6981.41           Healthy
           ...             ...             ...
   198     23854.73        24722.28        7611.53           Tumor

Since there are 1584 of these features in total, I need to select the ones that are most influential towards developing a Tumor.
My approach is described below. Is it correct?
The distributions of features are mostly log-normal. I've transformed each feature with a Box-Cox transformation to get approximately normal distributions. I scaled the values with Min-Max scaler to put them in range $[0,1]$.
miRNA1 has $100$ Healthy samples and $98$ Tumor samples. I should make a null hypothesis that Tumor samples have the same values as Healthy samples. I calculate mean and standard deviation for Tumor samples and Healthy samples, calculate the t-score and calculate the p-value, using significance level of $0.05$ and DF in this case is $97$. This is a two-tails test so it is $p$-value $\times 2$. If it's lower than $0.05$ I reject the null hypothesis and consider miRNA1 as a feature that impacts Tumor development, right?
 A: First, what you have is high-dimensional data. This alone poses some problems and you should use a method which was designed for this, which is better suited.
Second, a regular t-test is a bad idea in this case, it is a univariate test - meaning it does not consider multiple variables together and their possible interactions. Also, p-values are not meant to be used for feature selection.
Nonetheless, if you are fixed on a t-test, it would be better to use a permutation test to test for significance, as you have many variables which will lead to some serious corrections, when you adjust your p-values for multiple testing, and you will adjust them, right?
Finally, personally I would use LASSO regression to solve this, which is a better and simpler option, LASSO automatically performs feature selection and it considers all the variables together, rather than one by one.
A: even though there are better options for feature selection but t-test can also be part of your research. What you is did correct up to some extent but you need to take the t-score as your reference for your feature ranking. the higher the t-score value, the better the feature is. I would recommend information theory-based filter methods and you can take the order of features from a decision tree using information gain and based on my experience it worked far way better than the t-test (t-test cannot remove redundancy) and I was even better than embedded feature selection approaches like L1 penalized logistic regression and RF from the point of feature subset compactness.
