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I have mutational catalogues of 4 samples like below

> head(out)
        Responders1 Nonresponders1 Responders2 Nonresponders2
A[C>A]A       11744          13546        2897           3655
A[C>A]C        5144           7172        2295           2163
A[C>A]G         939           1257         290            279
A[C>A]T        6065           7997        2078           2088
C[C>A]A        8969          11155        2055           2582
C[C>A]C        4050           6657        1367           1173
>

 > dim(mut)
[1] 96  4
> 

Note: A[C>A]A - Single nucleotide base substitution

I want to look if mutational substitutions are different between samples

I have tried

wilcox.test(mut$Responders2,mut$Nonresponders2)

t.test(mut$Responders2,mut$Nonresponders2)

var.test(mut$Responders2,mut$Nonresponders2)
fisher.test(mut$Responders2,mut$Nonresponders2,simulate.p.value=TRUE)

kruskal.test(mut$Responders2,mut$Nonresponders2)

To find a difference but I think you may know a better test to find any difference here

This is base shift (mutation, substitution across genome of each patient). I guess mutations happen randomly but I don't know if mutational caller algorithm also pick them by randomness. I expect the shift of A (Adenine) to C (Cytosine) in a group of these patients be higher than the other group. This higher rate likely should be due to some background than randomness picked by algorithm. So I think null hypothesis is that rate of base shifting is not the same in both group. I also don't expect a normalised distribution of base shifting rather a poison distribution.

Can you help please?

Thanks

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Answer from @fabio-marroni, converted from comment:

You might try to use mutationalPatterns and see if the responders cluster together and separated from nonresponders.

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