How to check the mutual exclusiveness between two genes?
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4.4 years ago
newbie ▴ 130

I have a total of 4500 samples. Among them GeneA is mutated in 600 and GeneB is in 900.

Mutations in both GeneA and GeneB are 90

Mutations in only GeneA and no mutation in GeneB are 550

Mutations in only GeneB and no mutation in GeneA are 810

I see some people use fishers test to check the significance of mutual exclusivity between two genes. May I know how to do this?

mutation mutual exclusivity statistics • 1.4k views
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4.4 years ago
russhh 5.7k

If you're certain that

• your ability to detect mutation in the each gene is stable across the samples

• and that detecting a mutation in one gene shouldn't impede your ability to detect mutations in the other

... you can run Fisher's exact test in R like so:

First determine how many of the samples display each combination of mutations (A, B) (A-mut, B), (A, B-mut), (A-mut, B-mut)

# rows are A (unmutated, then mutated), columns are B (unmutated, then mutated)
counts <- matrix(
c(4500 - (90 + 550 + 810), # 3050 with no mutation
550, # (A-mut, B);
810, # (A, B-mut);
90 # (A-mut, B-mut)
), nrow = 2)

results <- fisher.test(counts)
results


Note that this is a two-tailed test: it determines if there are more comutated samples than expected, or less comutated samples than expected.

If you have prior reason to expect exclusivity between the two genes, you can do a one-tailed test:

fisher.test(counts, alternative = "less")


On the back-of-the-envelope, you could reason as follows: 4500 samples, ~ 15% are A-mut, ~ 20% are B-mut; so you'd expect around 3% of the samples to be comutated; but you've only observed 2%. To me, this doesn't sound too impressive, but since you've used so many samples, it is pretty impressive.

Are the samples all from the same dataset?

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thanks a lot for the reply and the clear explanation. So, if the p-value is less than 0.05 it is seen as they are mutually exclusive. Am I right?

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No. There is evidence that they are mutually exclusive, under the many many assumptions of the test.