Comments (6)
Hi,
I believe the program is running. But 1000 samples unfinished in 12 hours does sound a bit strange. Could you try a few samples first and see it works? Also, you can interrupt the current program and see what it is running.
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Hi @ShantaoL,
thanks for the reply. It is still running until now. I cancelled the program and rerun the analysis with only 10 samples, it finished in a few seconds with output expected. So any suggestion for my case?
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I try 800 samples, it works well, but when the number comes to 875, it stuck again.
may be related to the sample?
I will try to figure it out, and let you know when it is done.
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I think there is one (or more) sample between 801-875 causing the hang-up.
You can try to feed the rest one-by-one to the program (or binary search) and see what actually causes the hang-up.
Please do share your findings, it will help sigLASSO to be more robust in the future. Thanks!
from siglasso.
I think there is one (or more) sample between 801-875 causing the hang-up.
You can try to feed the rest one-by-one to the program (or binary search) and see what actually causes the hang-up.Please do share your findings, it will help sigLASSO to be more robust in the future. Thanks!
I Found the sample with only one mutation that result in such phenomenon, which should be excluded in data preprocessing.
I don't know why a sample with one mutation could trigger this problem, but I think the function should at a default parameter to filter the sample with too little mutations, May be 2?
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Thanks for sharing this! I agree with you we should have an error catch early on for this.
In general, samples with mutation counts under 10ish are unsuitable to call signatures, it won't be reliable.
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Related Issues (9)
- Fail due to glmnet error HOT 1
- Mutational signatures v3 upgrade HOT 1
- Mistaken Chromosome Name Mismatch Error HOT 1
- Error in context2spec(context_file) : Unexpected mutation context! HOT 2
- non-human genome
- Extremely slow with many samples HOT 1
- Error: all penalty factors are <= 0
- error in cv.glmnet()
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