estimating global dNdSCV ratios from subsets of coding mutations

[bkinnersley]

Hello,

Thank you for such a useful package! I have two queries:

1. When estimating per-gene dNdSCV ratios and confidence intervals using the geneci function, is it possible to calculate a "combined" ratio per-gene from the mis_mle and trunc_mle values (e.g. similar to the "wall" mle value from globaldnds? Or as you've mentioned previously does this usually tend to approximate mis_mle in any case?
2. The function "genesetdnds" is provided to estimate global dnds ratios from gene subsets (but making use of the full dataset for background modelling). If I wanted to instead estimate global dnds ratios for mutation subsets (e.g. predicted neoantigens versus not predicted neoantigens, clonal versus subclonal) how would you recommend going about this - would it be reasonable for example to simply restrict the input mutations dataframe to the different subsets in each case and run dNdSCV as normal?

Thanks very much

Best wishes

Ben

[Yunuuuu]

The same question, do you figure out it? I also want to combine per-gene mis_mle and trunc_ml

[Yunuuuu]

I want to do some analysis provided by TRACERx article: The evolution of lung cancer and impact of
subclonal selection in TRACERx (doi: https://doi.org/10.1038/s41586-023-05783-5)

The dN/dS point mutation estimate was calculated by combining the dN/dS estimates of missense, nonsense and splice-site substitutions calculated using the dndscv and geneci functions in the R package dNdScv. I have read their data into R. I don't know how to generate point_w point_w_low and point_w_high column (which should be the combined non-synonymous mutations), and they just didn't include this analysis in their supplement code. Is there any way to calculate this column?