I am working on 4 different plants. I have the (RNA-Seq) data from sequencing. I look for two events
E2, say, at certain positions in their genome. Let's say
E2 is the common one and
E1 is a special event. The positions I look for are identical across all 4 (in the genome). And let's say I see that the observation I have for 1 particular
P1 P2 P3 P4 E1 0 20 0 17 E2 100 80 100 120
P4 refers to plants and
E2 refers to the events.
E2 is more common. So, my objective is to actually check if
E1 occurs more often in one or more plants than in the others. If they occur at the same proportion in all, of course it is not interesting to me.
I have 2 questions: (I have already asked question 1 before but didn't get an answer)
Will a fisher test be right for this problem?
I hypothesize (Null) that the proportion of
E1is not different in occurrence between the 4 plants. Now, I set out to find if the proportion I have here is by any means significantly different. I use R,
fisher.test()and I get p-value=2.5e-10. So, I reject my Null hypothesis for this case because I find strong evidence against it.
Sometimes, I have an observation like this,
P1 P2 P3 P4 E1 0 20 0 17 E2 0 80 0 120
fisher.test() gives me a p-value of 0.147. So, I don't reject the Null hypothesis. However, from a biological point of view, I would consider this significant. However
fisher test answers the question I originally asked. I guess the proportion
0/0 for P1 and P3 are not useful (or not used).
So, my question is: Is it possible to modify the test such that it is sensitive even if
0 in 1 or more plants for a particular observation?
Having thought a bit to frame this post, I guess, in that case I have to ask a different question.
I look forward to your your suggestions!