saved section for generating revised dst
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1 changed files with 156 additions and 87 deletions
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@ -1238,10 +1238,12 @@ del(out_filename_metadata_poscounts)
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#%% Add column: aa_calcprop
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#%% NEW mappings: gene_LF2
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# gene_LF2: copy gene_LF1
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gene_LF2 = gene_LF1.copy()
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gene_LF2 = gene_LF1.copy()gene_LF3.groupby('mutationinformation').drtype_all_vals.apply(list)
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gene_LF2.index
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#%% Add total unique id count
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gene_LF2['id'].nunique()
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gene_LF2['mutationinformation'].nunique()
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gene_LF2['Mut'].nunique()
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total_id_ucount = gene_LF2['id'].nunique()
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total_id_ucount
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total_id_ucount2 = gene_LF2['sample'].nunique()
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@ -1275,37 +1277,23 @@ gene_LF2['total_id_ucount'] = total_id_ucount
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gene_LF2['maf'] = gene_LF2['snp_frequency']/gene_LF2['total_id_ucount']
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gene_LF2['maf'].head()
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#%% Mapping 1: column '<drug>', mutation_info
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gene_LF2['mutation_info'].value_counts()
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gene_LF2['mutation_info_v1'].value_counts()
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gene_LF2['mutation_info_orig'].value_counts()
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#%% Further mappings: gene_LF3, with mutationinformation as INDEX
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gene_LF3 = gene_LF2.copy()
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#=======================
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# column name: <drug>
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#=======================
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# mapping 1.1: labels
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dm_om_label_map = {dr_muts_col: 'DM'
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, other_muts_col: 'OM'}
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dm_om_label_map
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gene_LF2['mutation_info_labels'] = gene_LF2['mutation_info'].map(dm_om_label_map)
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# Assign index: mutationinformation for mapping
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gene_LF3 = gene_LF3.set_index(['mutationinformation'])
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gene_LF3.index
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gene_LF3['id'].nunique()
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gene_LF3['Mut'].nunique()
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gene_LF3.index.nunique()
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# mapping 1.2: numeric
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dm_om_num_map = {dr_muts_col: 1
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, other_muts_col: 0}
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all(gene_LF3['Mut'] == gene_LF3.index)
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gene_LF2['dm_om_numeric'] = gene_LF2['mutation_info'].map(dm_om_num_map)
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gene_LF2['dm_om_numeric_orig'] = gene_LF2['mutation_info_orig'].map(dm_om_num_map)
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gene_LF2['mutation_info'].value_counts()
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gene_LF2['mutation_info_labels'].value_counts()
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gene_LF2['mutation_info_orig'].value_counts()
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gene_LF2['dm_om_numeric'].value_counts()
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gene_LF2['dm_om_numeric_orig'].value_counts()
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#%% Mapping 2: column '<drtype>'
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#%% Mapping 1: column '<drtype>'
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#============================
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# column name: <drtype>
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#============================
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gene_LF2['drtype'].value_counts()
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gene_LF3['drtype'].value_counts()
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# mapping 2.1: numeric
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drtype_map = {'XDR': 5
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@ -1315,55 +1303,10 @@ drtype_map = {'XDR': 5
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, 'Other': 1
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, 'Sensitive': 0}
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gene_LF2['drtype_numeric'] = gene_LF2['drtype'].map(drtype_map)
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gene_LF3['drtype_numeric'] = gene_LF3['drtype'].map(drtype_map)
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gene_LF2['drtype'].value_counts()
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gene_LF2['drtype_numeric'].value_counts()
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#%% Mapping 3: column '<dst>', drug
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#============================
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# column name: <dst>
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#============================
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# copy dst column
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gene_LF2['dst'] = gene_LF2[drug] # to allow cross checking
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gene_LF2['dst'].equals(gene_LF2[drug])
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gene_LF2['dst_multimode'] = gene_LF2[drug]
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gene_LF2[drug].isnull().sum()
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gene_LF2['dst_multimode'].isnull().sum()
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#%% Further mappings: gene_LF3
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gene_LF3 = gene_LF2.copy()
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gene_LF3.index
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gene_LF3 = gene_LF3.set_index(['Mut'])
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gene_LF3.index
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gene_LF3['dst_multimode'].value_counts()
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gene_LF3['dst_multimode'].value_counts().sum()
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#%% Multimode: dst
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# For each mutation, generate the revised dst which is the mode of dm_om_numeric
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#=============================
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# Recalculation: Revised dst
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# max(multimode)
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#=============================
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# Get multimode for dm_om_numeric column
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#dm_om_multimode_LF3 = gene_LF3.groupby('mutationinformation')['dm_om_numeric_orig'].agg(multimode)
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dm_om_multimode_LF3 = gene_LF3.groupby('mutationinformation')['dm_om_numeric'].agg(multimode)
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dm_om_multimode_LF3
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# Fill using multimode ONLY where NA in dst_multimode column
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gene_LF3['dst_multimode'] = gene_LF3['dst_multimode'].fillna(dm_om_multimode_LF3)
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# Now get the max from multimode
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gene_LF3['dst_noNA'] = gene_LF3['dst_multimode'].apply(lambda x: np.nanmax(x))
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print(gene_LF3)
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#%% Revised Columns:IMPORTANT
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#%% Multimode: dst column
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#----------------------------
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# Revised dst column: Max
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#----------------------------
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# Finally created a revised dst with the max from the multimode
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gene_LF3['dst_mode'] = gene_LF3.groupby('mutationinformation')['dst_noNA'].max()
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gene_LF3['drtype'].value_counts()
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gene_LF3['drtype_numeric'].value_counts()
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#%% Multimode: drtype
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#=============================
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@ -1378,12 +1321,14 @@ gene_LF3['drtype_all_vals'] = gene_LF3['drtype_numeric']
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gene_LF3['drtype_all_names'] = gene_LF3['drtype']
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gene_LF3['drtype_all_vals'] = gene_LF3.groupby('mutationinformation').drtype_all_vals.apply(list)
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gene_LF3['drtype_all_names'] = gene_LF3.groupby('mutationinformation').drtype_all_names.apply(list)
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gene_LF3['drtype_all_vals'].head()
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gene_LF3['drtype_all_names'] = gene_LF3.groupby('mutationinformation').drtype_all_names.apply(list)
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gene_LF3['drtype_all_names'].head()
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#---------------------------------
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# Revised drtype: max(Multimode)
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#--------------------------------
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gene_LF3['drtype_multimode'] = gene_LF3.groupby(['mutationinformation'])['drtype_numeric'].agg(multimode)
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gene_LF3['drtype_multimode'] = gene_LF3.groupby('mutationinformation')['drtype_numeric'].agg(multimode)
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gene_LF3['drtype_multimode']
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# Now get the max from multimode
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@ -1394,26 +1339,150 @@ gene_LF3.head()
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# Revised drtype: Max
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#----------------------
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gene_LF3.head()
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gene_LF3['drtype_max'] = gene_LF3.groupby(['mutationinformation'])['drtype_numeric'].max()
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gene_LF3['drtype_max'] = gene_LF3.groupby('mutationinformation')['drtype_numeric'].max()
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gene_LF3.head()
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#%% Revised counts checks
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gene_LF3['dst_mode'].value_counts()
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foo = gene_LF3[['Mut', 'position', 'drtype', 'drtype_multimode', 'drtype_mode', 'drtype_max']]
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foo2 = foo.sort_values(['position', 'Mut'])
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###############################################################################
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#%% Mapping 2: column '<dst>', drug
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#=======================
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# column name: <drug>
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#=======================
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# mapping 1.1: labels
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dm_om_label_map = {dr_muts_col: 'DM'
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, other_muts_col: 'OM'}
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dm_om_label_map
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gene_LF3['mutation_info_labels'] = gene_LF3['mutation_info'].map(dm_om_label_map)
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# mapping 1.2: numeric
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dm_om_num_map = {dr_muts_col: 1
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, other_muts_col: 0}
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gene_LF3['dm_om_numeric'] = gene_LF3['mutation_info'].map(dm_om_num_map)
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gene_LF3['dm_om_numeric_orig'] = gene_LF3['mutation_info_orig'].map(dm_om_num_map)
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gene_LF3['mutation_info'].value_counts()
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gene_LF3['mutation_info_labels'].value_counts()
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gene_LF3['mutation_info_orig'].value_counts()
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gene_LF3['dm_om_numeric'].value_counts()
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gene_LF3['dm_om_numeric_orig'].value_counts()
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# Check value_counts: column '<drug>', mutation_info
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gene_LF3['mutation_info'].value_counts()
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gene_LF3['mutation_info_v1'].value_counts()
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gene_LF3['mutation_info_orig'].value_counts()
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#============================
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# column name: <dst>
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#============================
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# copy dst column
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gene_LF3['dst'] = gene_LF3[drug] # to allow cross checking
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gene_LF3['dst'].equals(gene_LF3[drug])
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gene_LF3['dst_multimode'] = gene_LF3[drug]
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gene_LF3[drug].isnull().sum()
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gene_LF3['dst_multimode'].isnull().sum()
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gene_LF3['dst_multimode'].value_counts()
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gene_LF3['dst_multimode'].value_counts().sum()
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#%% Multimode: dst
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# For each mutation, generate the revised dst which is the mode of dm_om_numeric
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#=============================
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# Recalculation: Revised dst
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# max(multimode)
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#=============================
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# Get multimode for dm_om_numeric column
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#dm_om_multimode_LF3 = gene_LF3.groupby('mutationinformation')['dm_om_numeric_orig'].agg(multimode)
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dm_om_multimode_LF3 = gene_LF3.groupby('mutationinformation')['dm_om_numeric'].agg(multimode)
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dm_om_multimode_LF3
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dm_om_multimode_LF3.isnull().sum()
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gene_LF3['dst_multimode_all'] = gene_LF3.groupby('mutationinformation')['dm_om_numeric'].agg(multimode)
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gene_LF3['dst_multimode_all']
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# Fill using multimode ONLY where NA in dst_multimode column
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gene_LF3['dst_multimode'] = gene_LF3['dst_multimode'].fillna(dm_om_multimode_LF3)
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gene_LF3['dst_multimode']
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#----------------------------------
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# Revised dst column: max of mode
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#----------------------------------
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# Finally created a revised dst with the max from the multimode
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# Now get the max from multimode
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#gene_LF3['dst_mode'] = gene_LF3.groupby('mutationinformation')['dst_noNA'].max() # this somehow is not right!
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#gene_LF3['dst_noNA'] = gene_LF3['dst_multimode'].apply(lambda x: np.nanmax(x))
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gene_LF3['dst_mode'] = gene_LF3['dst_multimode'].apply(lambda x: np.nanmax(x))
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# sanity checks
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gene_LF3['dst_noNA'].equals(gene_LF3['dst_mode'])
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gene_LF3[drug].value_counts()
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gene_LF3['dst_noNA'].value_counts()
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gene_LF3['dst_mode'].value_counts()
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foo = gene_LF3[['Mut', 'position', 'dst', 'dst_multimode', 'dst_noNA', 'dst_mode']]
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foo2 = foo.sort_values(['position', 'Mut'])
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print('\n------------------------------------------------------'
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, '\nRevised counting: mutation_info i.e dm om column'
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, '\n-----------------------------------------------------'
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, '\nRevised counting: mutation_info i.e dm om column\n'
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, '\n-----------------------------------------------------\n'
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, '\n----------------------------------'
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, '\nOriginal drug column count'
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, '\n----------------------------------'
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, '\n----------------------------------\n'
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, gene_LF3[drug].value_counts()
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, '\nTotal samples [original]:', gene_LF3[drug].value_counts().sum()
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, '\nTotal samples [original]', gene_LF3[drug].value_counts().sum()
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, '\n----------------------------------'
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, '\nRevised drug column count'
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, '\n----------------------------------'
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, '\nRevised drug column count\n'
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, '\n----------------------------------\n'
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, gene_LF3['dst_mode'].value_counts()
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, '\nTotal samples [revised]:', gene_LF3['dst_mode'].value_counts().sum()
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, '\nTotal samples [revised]', gene_LF3['dst_mode'].value_counts().sum()
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, '\n----------------------------------'
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, '\nRevised drug column count: dst_noNA\n'
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, '\n----------------------------------\n'
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, gene_LF3['dst_noNA'].value_counts()
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)
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#%% Create revised mutation_info_column based on dst_mode
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#---------------------------------------
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# Create revised mutation_info_column
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#---------------------------------------
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# Will need to overwrite column 'mutation_info_labels', since downstream depends on it
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# Make a copy you before overwriting
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#gene_LF3['mutation_info_labels_v1'] = gene_LF3['mutation_info'].map(dm_om_label_map)
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gene_LF3['mutation_info_labels_v1'] = gene_LF3['mutation_info_labels']
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gene_LF3['mutation_info_labels_v1'].value_counts() == gene_LF3['mutation_info_labels'].value_counts()
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# Now overwrite
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gene_LF3['mutation_info_labels_dst'] = gene_LF3['dst_mode'].map({1: 'DM', 0: 'OM'})
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gene_LF3['mutation_info_labels'] = gene_LF3['dst_mode'].map({1: 'DM', 0: 'OM'})
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if (gene_LF3['mutation_info_labels_dst'].value_counts() == gene_LF3['mutation_info_labels'].value_counts()):
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print('\nRevised mutation_info colum created')
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gene_LF3 = gene_LF3.drop(['mutation_info_labels_dst'], axis = 1)
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else:
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print('\nmutation info labels numbers mismatch'
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, '\nPlease check section for mapping dst_mode to labels')
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gene_LF3['mutation_info_orig'].value_counts()
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#gene_LF3['mutation_info_labels'].value_counts()
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gene_LF3['mutation_info_labels_orig'] = gene_LF3['mutation_info_orig'].map(dm_om_label_map)
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gene_LF3['mutation_info_labels_orig'].value_counts()
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# %% sanity check for the revised dst
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gene_LF3[drug].value_counts()
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gene_LF3[drug].value_counts().sum()
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gene_LF3['mutation_info_labels_orig'].value_counts()
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gene_LF3['dst_mode'].value_counts()
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gene_LF3['dst_mode'].value_counts().sum()
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# direct comparision
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gene_LF3['dst'].value_counts()
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gene_LF3['mutation_info_labels'].value_counts()
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#%%
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