Comments (3)
Hi Jeremy,
Sorry for the late reply. These are two interesting questions, see my answers below:
-
From what we have observed only gene usage and/or alleles sequences vary among the recombination machinery of different individuals. If you were to have a few hundreds of out of frame sequences I would have recommended to re-learn only the gene usage distributions. Now in your case the best would probably be to use the provided model as such. Anyway, these gene usage variability is not what's controlling most of Pgen variations.
-
There are two different things here: the number of scenarios explored by IGoR and the number of scenarios that IGoR outputs. Even by specifying
--scenarios 50
IGoR will explore many more of them, however only 50 of them will be written into file in the output directory. What is controlling the number of scenarios IGoR explores during an Expectation-Maximization step are the--P_ratio_thresh
and/or--MLSO
commands. In theory the more scenarios have been explored the best, in practice there is a balance with runtime, but the probability ratio threshold should not be set too high.
Hope this answers your questions
from igor.
Thanks for the explanation! I find that setting --P_ratio_thresh to 0.0 causes issues (every Pgen comes back as nan) but I can set it to extremely small values (e.g., 1E-10) without issue. What is the default P_ratio_thresh? (Perhaps that could be added to man igor).
Cheers.
from igor.
Mmm that is odd, as explained in here setting it to 0.0 should explore all possible scenarios (yielding a very slow execution time) at first thought I don't see why this should return nan. Could you attach a sample of the pgen, and inference_logs files for debugging purposes?
The default value for this parameter is 10^{-5}, I actually thought it was in the README, this will be added, thanks for pointing this out!
Thanks!
from igor.
Related Issues (20)
- File not found when IGoR is installed locally HOT 1
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from igor.