update enrichment analysis #15
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- line lenght restriction to 100 characters
maybe the dataset is not he best one to test enrichment analysis (few diff. reg. protein groups)
how many proteins/genes do need to be rejected to be considered valid. Before it was at least 2 genes.
- will be tested by building docs (maybe add a unittest later)
not sure if the example is the best.
enryH
commented
Nov 25, 2024
| @@ -308,7 +388,8 @@ def run_enrichment( | |||
| num_background = num_background[0] | |||
| else: | |||
| num_background = 0 | |||
| if method == "fisher" and num_foreground > 1: | |||
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to discuss. should we mistrust enrichements which are based on single hits?
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I am not sure they would be significant but filtering them would reduce the number of tests, which may be good in terms of performance.
- maybe it was there for compability, but then we could add kwargs to the function to catch any other arguments (without using these)
- NA can lead to float columns instead of 0. to investigate
enryH
commented
Nov 26, 2024
| _rejected, _pvals_corrected, _, _ = multitest.multipletests(p[mask], alpha, method) | ||
| pval_corrected[mask] = _pvals_corrected | ||
| rejected = np.full(p.shape, np.nan) | ||
| rejected[mask] = _rejected |
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ToDo: add a test that ensures that 1.0 and 0.0 are interpreted as bool.
enryH
commented
Nov 28, 2024
enryH
commented
Nov 28, 2024
enryH
commented
Nov 28, 2024
enryH
commented
Nov 28, 2024
resolve things discussed with Alberto.
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double check docstrings of module (for rendering) |
- prepare to defien a common return type by specifying message TYPE_COLS_MSG
sphinx deals differently with parsing the strings...
…orrectly otherwise numpy notation should work fine
…imes - default parameter is rejected, so it is easier to not mistype parameter name
works locally. see: https://stackoverflow.com/a/14414965/9684872
- output should be tsv due to how pandas return is constructed for now
- enrichment only separate for up- and down regulated protein groups - keep hint on to do for PTM dataset
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Hi, I checked the documentation notebook and the table showing the functional PCA loadings shows multiple GOs per row when there should be only one GO per row. I wonder if you are annotating each protein with all the associated terms (separated by ;)? 
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Go term annotations had several Go terms concatenated using `;`, which are now split up. Performance can be improved.
annotation field contains identifier again
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