Closed joerivstrien closed 3 years ago
Turns out T5 is also the first sample in the output (the order of samples in the output is random I guess?), so thats why it started with T5. In the end clustering worked for 2 out of 6 samples, for all the other samples maximum recursion depth error occurred.
Nice that the program continues anyway and produces an output with those that succeeded!
I can look into why the clustering code produces this error?
Using google I found out that the implementation of hierarchical clustering of scipy is mainly used for dendogram visualizations and thus isn't able to compute large dendograms. I found this implementation and applied in the code, tested it and the clustering step has become faster and can cluster more proteins than before. So, this issue has been solved.
When running the tool on a protein_groups file with 6 samples, (each having 60 slices) an error occurs during the clustering step.
The GUI displays the following error: An exception occurred while applying clustering on a sample Maximum recursion depth exceeded in comparison.
relevant lines in log file before error occurs: INFO:root:Step 4, cluster the fractions per sample using hierarchical clustering. INFO:root:Start hierarchical clustering for sample T5
(not sure why it starts clustering T5? maybe something went wrong with parsing the other samples? Samples are T1 - T6)