Hi,
I am performing a shuffle of reads from a ChIP-Seq experiment over some features in the genome and since I do not know the original distributions I am trying to perform an observed to expected calculation to see which features are enriched. Boiled down this means I have counts of reads mapping to feature x, then I perform a shuffle of the reads over the entire genome, count those and then calculate the count of how many reads mapped to feature x by chance. Unfortunately, I am not quite sure on how to get a p-value from this.
I hope you can help me with this. Thank you very much!
3 answers
This looks like a permutation test. Your p-value would be the number of times the permutations give more reads mapping to the feature under consideration divided by the number of permutations. And if you're doing multiple tests (i.e. testing many features), you should do a correction for multiple testing. Bonferroni is fine but too conservative, you should prefer FDR.
Thanks for the help! Will the permutation test p-value also apply if I process the data ? Say I count the reads and do a log transform of the counts. Or better: Does the formula hold true, when I compare the values after I do some sort of processing with it as long as I do the same processing with both, the observed data and the permuted data ? I noticed that Bonferroni will be rather conservative, thanks for the advice. I will use FDR.
You can do the permutation test after you have processed the data.
Try the fisher exact test. The contingency table could be something
| By random -- | In your experiment | |
| # Reads mapping to Feature X | _ | _ |
| # Reads not mapping to X | _ | _ |
Yes, that makes sense
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