429 Plots for PAE/pLDDT based CL validation#435
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NeleRiediger
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Testing went fine overall. I just noticed that for different validation methods of the same protein the number of crosslinks shown in the plots doesn't always match. Also the number of crosslinks shown in the plots doesn't always match the amount shown in the visualization. (I tested with monomer O43242) This is probably just due to overlapping or outliers, but I wanted to make sure there is no bug in it.
(Also I believe this is not in the scope of this PR, but I noticed that when the manual bounds method is chosen as a default when the step was newly created, the input fields for the bounds aren't shown. They only appear when I select the manual bounds method in the dropdown again.)
I made some comments in the code, but it generally looked good too.
| max_dist_delta = max(cl_results_df["distance_delta"]) | ||
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| xmin = -1 | ||
| if np.log10(xmin) > min_dist_delta: |
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I'm a little confused here, wouldn't this always be NaN?
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Messed that up, should be 10**xmin. Fixed now
| max_dist_delta = max(cl_results_df["distance_delta"]) | ||
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| xmin = -1 | ||
| if np.log10(xmin) > min_dist_delta: |
I believe this is due to overlap, as I don't hide anything. I count 7 CLs both in the plot and in the visu and one visual overlap for a CL with a Min. PAE of 8. Would be cool if you could double check that again to ensure this isn't a frontend bug on your side that we need to look at |
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Yes, that checks, seems to work fine now :) |
Description
fixes #429
Adds new plots for the PAE/pLDDT based CL validation methods. The plots are scatter plots that display the absolute deviation of the CL length compared to the predicted distance on the x-axis and the used PAE or avg. pLDDT on the y-axis.
Changes
crosslinking_validation.py, added the plots.Testing
Use a typical CL validation workflow and test out the plots and see if they make sense both for monomers and for multimers. Make sure to test all validation strategies.
PR checklist
Development
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blackCode review