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Model does not converge #6
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Hi! This usually happens when there are some unrealistically high expressing genes in the dataset. But from I have seen the optimization still converges for the remaining genes. It is just that the total or average loss is nan. So you can either filter very high expressing genes out before, or just leave it as it is. |
Thank you for the reply! Is the correction calculated by the model different for every gene in one sample? I thought it just finds a per-sample correction |
yes it is different for every gene |
Is there a way to see for which genes the model did not converge? |
No and I think the optimization never fully converges. However, the prior distributions and the starting point for the optimization are already sensible background binding values for all genes (specifically the mean of negative probes in each sample). So the values should be ok even after short optimisations. |
I tried twice, checked the input was correct but this is what I get:
Should I be running on all samples and genes or filter first?
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