Question: Fisher'S Exact Test For Gene Rank Enrichment
1
gravatar for The
4.4 years ago by
The90
United States
The90 wrote:

I'm trying some metrics to filter a list of gene names with associated numerical values which correspond to their abundance.

I'm looking for some interesting genes and trying to see if they come up to the top region of the ranked gene list(ranked by abundance) before and after filtering. Please see Fig. enclosed enter image description here I wish to see if the ranking of my gene of interest (G) has changed significantly before and after filtering and I'm thinking to do a Fisher's Exact test for proportions to ascertain the significance.

So given the rank of the gene of interest I'm willing to look at the number of genes above and below the gene in rank with and without the filtering and compare their proportions using FE Test

If in the UN-Filtered data there are 200 and 2800 genes above and below G and 30 and 100 in the Filtered data respectively then I can do the FE test as follows (in R):

    >my.mat <- matrix(c(30,100,200,2800),nrow=2,byrow=TRUE)
    >my.mat
         [,1] [,2]
    [1,]   30  100
    [2,]  200 2800

>fisher.test(my.mat,alternative="two.sided")

Please let me know whether I can do a Fisher Exact test AT ALL for such kind of measurement ?

Thanks

• 3.0k views
ADD COMMENTlink modified 4.3 years ago • written 4.4 years ago by The90
2

I would suggest a permutation test. permute randomly your lists many times and see how frequently you get a change as big as the real one.

ADD REPLYlink written 4.4 years ago by Martombo2.0k
1

In general I don't see major flaws with that. However, Fisher's assumptions are that all observations are independent of each other which in fact might not be the case w.r.t the gene you are looking at.

ADD REPLYlink written 4.4 years ago by Phil S.660

Many thanks Phil and Martomobo. Phil can you please explain how Fisher's assumption of independence can be violated here?

ADD REPLYlink written 4.4 years ago by The90
1

Imagine you are investigating a gene which codes for a "master" regulator somewhere upstream in a pathway, or even regulates a whole pathway. Therefore, your gene of interest will influence the level of abundance in a significant manner. Saying that, it also influences the position of a particular gene /or some genes in your ranking based on abundances. I know it is a special case but you have to, at least, keep in mind that some of the genes are not independent from each other. I guess, if you geneset is large enough you can ignore this fact to some extend that is why i would give it a try. Imho, it would be nice to see whether the permutation test gives comparable results to "fisher's" approach.

ADD REPLYlink written 4.4 years ago by Phil S.660
0
gravatar for The
4.3 years ago by
The90
United States
The90 wrote:

And what would it be more appropriate to use ?

>fisher.test(my.mat,alternative="two.sided")

Or

>fisher.test(my.mat,alternative="greater")
ADD COMMENTlink written 4.3 years ago by The90
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