Compute selection for known hotspots analysis

[Copilot]

Thanks for asking me to work on this. I will get started on it and keep this PR's description up to date as I form a plan and make progress.

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This section details on the original issue you should resolve

<issue_title>Compute selection for known hotspots</issue_title>
<issue_description>Using the information provided in the optional hotspots file of deepCSA, we could provide a computation of whether the (known) tumor hotspots are selected or not for each specific gene.

This could be done by taking the output of a site comparison process, and then also the file with the hotspots annotation, and with these two you could:

1. Annotate whether each position is or is not a known hotspot.
2. Group positions based on Gene and Hotspot(Yes/No). Apply the sum to the expected and observed columns independently.
3. Perform 3 different groupings based on: site, amino acid change and amino acid, and for each of them...
4. Compute the updated Obs/Exp ratios and the p-values in the same way as it is being done for the individual site selection computations.
5. Apply multiple testing correction to the p-values.

For this we need to have a new nf module, a new python script and import some of the functions that are already present.</issue_description>

Comments on the Issue (you are @copilot in this section)

[FerriolCalvet]

This branch has now been tested and it works well. there are only a couple of things left to test/understand/decide.

• Yes includes the hotspots, can we confirm that No includes all non-hotspot positions even if not mutated?
• Do we want to report in each row how many positions had the chance to be mutated in each category, let's say that for hotspots in KRAS we have 3 aminoacid positions and 151 positions for non-hotspots, this way we know how are thing computed more accurately.

I would double check the first point and make a decision on the second one and then we can merge this branch.