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comparison process_intensities.xml @ 5:afa3cb2110eb draft

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planemo upload for repository https://github.com/goeckslab/tools-mti/tree/main/tools/mti-utils commit bc438db690e41823909b32b693f297d942433a43
author goeckslab
date Thu, 11 Jul 2024 22:41:26 +0000
parents 5d541df02496
children
comparison
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4:5d541df02496 5:afa3cb2110eb
25 marker_df = pd.read_csv('$channel_csv') 25 marker_df = pd.read_csv('$channel_csv')
26 26
27 markers_to_normalize = marker_df['marker_name'].to_list() 27 markers_to_normalize = marker_df['marker_name'].to_list()
28 28
29 #if $AF_method.select_method == 'SBR': 29 #if $AF_method.select_method == 'SBR':
30 AF_markers = [x for x in list(marker_df['${AF_method.AF_col}'].unique()) if x != 'None'] 30 AF_markers = [x for x in list(marker_df['${AF_method.AF_col}'].unique()) if x not in ['None',np.nan]]
31 print(f'Detected {quant[AF_markers].eq(0.0).any(axis=1).sum()} cells with AF values of zero in the dataset') 31 print(f'Detected {quant[AF_markers].eq(0.0).any(axis=1).sum()} cells with AF values of zero in the dataset')
32 32
33 #if $AF_method.AF_filter == 'filter': 33 #if $AF_method.AF_filter == 'filter':
34 pre_filter_count = len(quant) 34 pre_filter_count = len(quant)
35 quant = quant.loc[quant[AF_markers].ne(0.0).any(axis=1),:] 35 quant = quant.loc[quant[AF_markers].ne(0.0).any(axis=1),:]