How many results do you have for your samples? If it is just 1, or say 3 if you did it in triplicate, then there is no distribution to calculate. The question you are trying to answer is whether there are differences between your individual isolates and your control right? So you've got expression levels for each gene in each isolate and an expression level for the control is that correct? To answer your question, I'd need to know how many results you have for each isolate and each gene. If you've got say 1 gene expression result for each gene and each isolate, then there is not distribution to work out. If you've got 3 gene expression results for each gene and each isolate then just assume it is normal. If you've got say 100 gene expression results for each gene and each isolate then do a Shapiro test on your results and a QQ plot to see if it is normal or not. If you've only got a small number of results for each gene and isolate, then your data won't be powerful enough to do a robust statistical analysis. Non-parametric tests in particular need a large number of results to be reliable. In that case you're better off just using a parametric test and assuming normality. Many parametric tests are quite robust against data that are not completely normal. On the other hand, if you've only got 1 result for each gene and isolate, then there is no distribution to test and no statistical comparison to run.

Without a greater understanding of your experimental design, including numbers of replicates, it is difficult to give you a proper answer.

I agree.

Dear David, thank you so much for your answer.

I did triplicate of each sample per gene, so i have 69 results+3 control (ATCC) results for each gene.

Also i would like to know is it better to use dct values when trying to find out whether or not data distribution is normal and in later statistical analysis, and to use fold change when trying to show graphical data?