# Compute the significance of the overlap between 2 or more gene sets

I was able to compute the significance of the overlap between 2 gene sets using the cdf function of scipy hypergeometric distribution.

I wish to be able to perform the same calculation for more than 2 gene sets; should I use the multivariate hypergeometric distribution cdf function for that?

Are there any websites that provides the same calculations over gene sets so I can validate my results?

• Not so trivial, if you want to use a distribution. stats.stackexchange.com/questions/52004/… Sep 3 '20 at 13:32
• Easiest way is to simulate the data, and see how many times you end up with an intersection set larger than what you see Sep 3 '20 at 13:33

This is a shot at it, first an example dataset:

import matplotlib.pyplot as plt
import numpy as np
import functools
from matplotlib_venn import venn3

# define universe
uni = ["gene"+str(i) for i in range(1000)]
# some overlap
gs1 = uni[250:300] + uni[900:950]
gs2 = uni[:300]
gs3 = uni[250:500]


The ever amazing venn diagram:

venn3([set(gs1),set(gs2),set(gs3)],set_labels=["gs1","gs2","gs3"]) Then a function to draw a set with length equivalent of each set, randomly from the universe and find length of intersection (all 3):

def sim_intersect(uni,set_lengths):
randomsets = [np.random.choice(uni,n) for n in set_lengths]
return len(functools.reduce(np.intersect1d,randomsets))


We run this 1000 times:

permuted_values = [sim_intersect(uni,[len(gs1),len(gs2),len(gs3)]) for i in range(1000)]

plt.hist(permuted_values,bins=range(50)) The probability of observing the starting result, using (B+1)/(M+1) as estimator, see this post:

(sum(np.array(permuted_values)>obs_n)+1)/(1000+1)
0.000999000999000999

• How would a population size parameter fit in this approach? Doesn't the p value should be affected by the total 'available genes' ? For example,. if I have two gene sets A, B. |A| = 250, |B| = 220, |A&B| = 65 As far as I understand, the p value should change dramatically if the population size is 300 or 30,000. Could you please explain where/how does it fit in this approach? Sep 6 '20 at 17:43
• Yes you are right, the total available genes would matter. In the code above, you would define the set of available genes as uni . It is not like a hypergeometric where you input a number of genes parameter. You simply simulate the results and see how likely you end up with a result as extreme as yours Sep 6 '20 at 17:53
• easiest thing to do is to re-run the above simulation with a small "universe" Sep 6 '20 at 17:54