I have a data set similar to the one below. The real data set has 89 values in each column. I'm looking at the expression of RNA between two different treatments (treatment $X$ and treatment $Y$).
GENE Xtest1 Xtest2 Xtest3 Ytest1 Ytest2 Ytest3 0 FOXO 34 193 12.0 23 23 1 1 TP53 67 432 0.4 234 34 243 2 LRU0046.3 21 543 234.0 545 6 65 3 MUC2 768 346 12.0 23 3 4 4 MUC16 100 234 456.0 435 234 243
I'm trying to work out the best statistics test to run in order to generate $P$-values and see if there is a significant difference between [
Xtest3] vs [
Ytest3]. I considered a $t$-test, however the distribution of the values is not within a Gaussian distribution. However, when I take the log of each value the Gaussian distribution is almost normal (the left tail is missing).
Can I get away with converting my data to log values (some downstream analysis actually will require me to convert to log values), or should I use a non-parametric test?