Which tests and steps to do?

Hey there! I am new to this so I would really really appreciate any help! I haven't got statistical education but still I have to use it, so I want to do it right!
I'm doing proteomics and I work with drought stress. I wanted to study two different fractions: the soluble (sol) proteins and the microsomal (mic) proteins and three different drought levels: control (C), moderate drought (MD) and severe drought(SD), so I got 6 different conditions in total. Also, for 4 conditions I got 5 replicates but for C-mic I got 10 and for MD-mic I got 8. I got around 400 proteins in total, so my excel looks like the one I attached (but with only 11 proteins)

I want to know if each protein changes from control conditions to moderate drought and/or to severe drought, and also know which of those that change are the most responsible for the differences between treatments.

*My data values are Spectral Counts, which I divided by the protein molecular weight and x100. Then I performed a log2 to make the sampels more normal.

To test for normality of each condition I performed a Saphiro-wilk test, and to test for difference sin variances a fisher test, both using excel (saphiro wilk with RealStats pack for excel).

then I performed a type2 t student test using excel again, one test per protein comparing Cmic with MDmic, Csol with MDsol, Cmic with SDmic and Csol with SDsol. For the cases in which the variances were different I performed a type 3 t student test.

I dont know if this is the most suitable way to do this, I was checking the ONE WAY and TWO WAY anova tests, but it would just tell me that the menas of CMm-MDm-SDm are different, right?and to know where that difference is I should perform a t test...am I right?

I didnt only do statistics but calculated the fold change, so combining LFC with t student I selected the proteins that significantly changed.

Ok, assuming I am right about what I said above now that I know which proteins are statistically different I want to know which of them are responsible for the differences between the Cmic samples-MDmic samples and SDmic samples, and also the same for the sol samples. I thought of doing a PCA analysis. I guess I cant do that in excel and should use SPSS, but my license is not working anymore (sigh), maybe with sigmaplot? Anyways, I guess that to make it more accurate I first pperform a PCA entering all the protein data for Cmic and for MDmic. That should give me the loading, which are the proteins that affect the separation of Cmic and MDmic the most? What about an ICA?

Also...many sampels had different variances, can I then perform a PCA/ICA?

Also, as you see, I treated the different protein fractions (mic and sol) separately but maybe it would also be interesting to do statistics using that variable as well (not drought level only, but fraction too). Well there I am even more lost!

Can anybody help me please? Thank you so so much really