more tidying and formatting for combining_dfs.py. Hopefully no more after today
This commit is contained in:
parent
c48fa1dbb0
commit
1f266c4cb8
2 changed files with 217 additions and 84 deletions
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@ -227,26 +227,97 @@ outfile_stab_struc = outdir + out_filename_stab_struc
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# end of variable assignment for input and output files
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#%%############################################################################
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print('\n==================================================='
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, '\nCombining dfs for gene:', gene.lower()
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, '\nDrug name:', drug
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, '\n====================================================')
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#=====================
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# some preprocessing
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# variable setting
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#=====================
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gene_pdb_f = gene.lower() + '_complex.pdb'
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gene_with_offset = ['alr', 'katg', 'rpob']
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#geneL_snap2 = ['alr', 'katg', 'rpob']
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gene_with_offset_missing_aa = ['embb']
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if gene.lower() == 'alr':
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offset_val = 34
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if gene.lower() == 'katg':
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offset_val = 23
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if gene.lower() == 'rpob':
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offset_val = 28
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if gene.lower() == 'embb':
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offset_val = 23
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missing_aa_start = 248
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missing_aa_end = 269
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missing_res_offset = missing_aa_end - missing_aa_start
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if gene.lower() == 'gid':
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offset_val = 0
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if gene.lower() == 'pnca':
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offset_val = 0
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#===========
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# DSSP
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#===========
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print('\nFormatting dssp_df:'
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, '\nSelecting chain:', sel_chain, 'for gene', gene.lower())
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dssp_df = dssp_df_raw[dssp_df_raw['chain_id'] == sel_chain]
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print('\nDim of dssp_df:', dssp_df.shape)
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dssp_df['chain_id'].value_counts()
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#===========
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# KD
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#===========
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kd_df.shape
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if gene.lower() in gene_with_offset:
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print ('\nRunning analysis for gene with offset:', gene.lower()
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, '\nOffset value:', offset_val
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, '\nFormatting kd_df with offset value'
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, kd_df.shape)
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kd_df['position'] = kd_df['position'] + offset_val
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# geneL_kd = ['alr']
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# if gene.lower() in geneL_kd:
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# print('\nRunning gene:', gene.lower()
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# ,'\nChecking start numbering')
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if gene.lower() in gene_with_offset_missing_aa:
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print ('\nRunning analysis for gene with offset and missing aa:', gene.lower()
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, '\nOffset value:', offset_val
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, '\nMissing residues gap:', missing_res_offset
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, '\nFormatting kd_df with offset value'
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, '\nDim of kd_df:', kd_df.shape)
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if kd_df['wild_type_kd'].str.contains('X').any():
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print('\nDetected X in wild_type_kd'
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, '\nRunning gene:', gene.lower()
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, '\nChecking start numbering')
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kd_df = kd_df[~kd_df['wild_type_kd'].str.contains('X')]
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# First correct offset, then correct GAP
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print('\nFirst correcting offset')
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kd_df['position'] = kd_df['position'] + offset_val
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print('\nNow correcting gap')
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kd_df.loc[kd_df['position'] >= 248, 'position'] = kd_df['position'] + missing_res_offset
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else:
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print ('\nRunning analysis for gene WITHOUT offset:', gene.lower()
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, '\nNo offset value:'
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, '\nUsing kd_df as is read'
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, kd_df.shape)
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print('\nChecking the position numbers from the 3 dfs: dssp, kd and rd')
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dssp_kd_rd_pos_checks = pd.DataFrame ({"dssp_pos": dssp_df['position']
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, "kd_pos": kd_df['position']
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, "rd_pos": rd_df['position']})
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dkr_pos_check = [
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dssp_kd_rd_pos_checks['dssp_pos'].equals(dssp_kd_rd_pos_checks['kd_pos'])
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, dssp_kd_rd_pos_checks['dssp_pos'].equals(dssp_kd_rd_pos_checks['rd_pos'])
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, dssp_kd_rd_pos_checks['kd_pos'].equals(dssp_kd_rd_pos_checks['rd_pos'])
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]
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if all(dkr_pos_check):
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print('\nPASS: position check passed for dssp, rd and kd dfs ')
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else:
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print('\nFAIL: position check Failed for dssp, rd and kd dfs'
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, '\nNumbering mismatch. Please check these dfs.')
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sys.exit()
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#%%============================================================================
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#===========
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# FoldX
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#===========
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@ -439,26 +510,14 @@ consurf_df.shape
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consurf_df.columns
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print('\nRenaming cols and assigning pretty column names')
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geneL_consurf = ['alr', 'katg', 'rpob']
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if gene.lower() in geneL_consurf:
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if gene.lower() in gene_with_offset:
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consurf_df = consurf_df.rename(columns={'POS' : 'position_consurf'})
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#---------------------------
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# Specify the offset
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#---------------------------
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print('\nAdding offset value for gene:', gene.lower())
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if gene.lower() == 'alr':
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offset_val = 34
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print('\nUsing offset val:', offset_val)
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if gene.lower() == 'katg':
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offset_val = 23
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print('\nUsing offset val:', offset_val)
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if gene.lower() == 'rpob':
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offset_val = 28
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print('\nUsing offset val:', offset_val)
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print('\nFormatting consurf df'
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, '\nAdding offset value for gene:', gene.lower()
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, '\nUsing offset val:', offset_val)
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consurf_df['position'] = consurf_df['position_consurf'] + offset_val
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@ -586,9 +645,7 @@ snap2_df.shape
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#----------------------
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# rename colums
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#----------------------
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geneL_snap2 = ['alr', 'katg', 'rpob']
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if gene.lower() in geneL_snap2:
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if gene.lower() in gene_with_offset:
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print('\nReading SNAP2 for gene:', gene.lower()
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, '\nOffset column also being read'
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, '\nRenaming columns...'
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@ -665,9 +722,7 @@ else:
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snap2_df.dtypes
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snap2_df.columns
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geneL_snap2 = ['alr', 'katg', 'rpob']
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if gene.lower() in geneL_snap2:
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if gene.lower() in gene_with_offset :
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print('\nSelecting cols SNAP2 for gene:', gene.lower())
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snap2_df_f = snap2_df[['mutationinformation'
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, 'mutationinformation_snap2'
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@ -685,7 +740,6 @@ else:
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#%%============================================================================
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# Now merges begin
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print('==================================='
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, '\nFirst merge: mcsm + foldx'
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, '\n===================================')
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@ -715,7 +769,7 @@ print('\n\nResult of first merge:', mcsm_foldx_dfs.shape
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mcsm_foldx_dfs[merging_cols_m1].apply(len)
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mcsm_foldx_dfs[merging_cols_m1].apply(len) == len(mcsm_foldx_dfs)
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#%% for embB and any other targets where mCSM-lig hasn't run for ALL nsSNPs.
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#%% for embB: mCSM-lig hasn't run for ALL nsSNPs.
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# Get the empty cells to be full of meaningful info
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if mcsm_foldx_dfs.loc[:,'wild_type': 'mut_aa_3lower'].isnull().values.any():
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print ('\nNAs detected in mcsm cols after merge.'
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@ -776,9 +830,8 @@ mcsm_foldx_deepddg_dfs = pd.merge(mcsm_foldx_dfs
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, deepddg_df
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, on = 'mutationinformation'
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, how = "left")
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mcsm_foldx_deepddg_dfs['deepddg_outcome'].value_counts()
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ncols_deepddg_merge = len(mcsm_foldx_deepddg_dfs.columns)
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mcsm_foldx_deepddg_dfs['deepddg_outcome'].value_counts()
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mcsm_foldx_deepddg_dfs['position'] = mcsm_foldx_deepddg_dfs['position'].astype('int64')
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@ -792,9 +845,6 @@ dssp_df_raw.shape
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kd_df.shape
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rd_df.shape
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dssp_df = dssp_df_raw[dssp_df_raw['chain_id'] == sel_chain]
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dssp_df['chain_id'].value_counts()
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#dssp_kd_dfs = combine_dfs_with_checks(dssp_df, kd_df, my_join = "outer")
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merging_cols_m2 = detect_common_cols(dssp_df, kd_df)
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dssp_kd_dfs = pd.merge(dssp_df
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@ -826,18 +876,16 @@ dssp_kd_rd_dfs[merging_cols_m3].apply(len) == len(dssp_kd_rd_dfs)
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#%%============================================================================
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print('==================================='
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, '\nFourth merge*: fourth merge + consurf_df'
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, '\nFifth merge: fourth merge + consurf_df'
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, '\dssp_kd_rd_dfs + consurf_df'
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, '\n===================================')
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#dssp_kd_rd_dfs = combine_dfs_with_checks(dssp_kd_dfs, rd_df, my_join = "outer")
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merging_cols_m3_v2 = detect_common_cols(dssp_kd_rd_dfs, consurf_df)
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dssp_kd_rd_con_dfs = pd.merge(dssp_kd_rd_dfs
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, consurf_df
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, consurf_df_f
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, on = merging_cols_m3_v2
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, how = "outer")
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ncols_m3_v2 = len(dssp_kd_rd_con_dfs.columns)
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print('\n\nResult of fourth merge*:', dssp_kd_rd_con_dfs.shape
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, '\n===================================================================')
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dssp_kd_rd_con_dfs[merging_cols_m3_v2].apply(len)
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@ -845,8 +893,8 @@ dssp_kd_rd_con_dfs[merging_cols_m3_v2].apply(len) == len(dssp_kd_rd_con_dfs)
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#%%============================================================================
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print('======================================='
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, '\nFifth merge: Second merge + fourth merge'
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, '\nmcsm_foldx_dfs + dssp_kd_rd_dfs'
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, '\nSixth merge: Second merge + fifth merge'
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, '\nmcsm_foldx_dfs + dssp_kd_rd_con_dfs'
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, '\n=======================================')
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#combined_df = combine_dfs_with_checks(mcsm_foldx_dfs, dssp_kd_rd_dfs, my_join = "inner")
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@ -854,56 +902,109 @@ print('======================================='
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#combined_df = pd.merge(mcsm_foldx_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how = "inner")
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#combined_df_expected_cols = ncols_m1 + ncols_m3 - len(merging_cols_m4)
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# with deepddg values
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merging_cols_m4 = detect_common_cols(mcsm_foldx_deepddg_dfs, dssp_kd_rd_dfs)
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combined_df = pd.merge(mcsm_foldx_deepddg_dfs
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, dssp_kd_rd_dfs
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# Detect merging cols
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merging_cols_m4 = detect_common_cols(mcsm_foldx_deepddg_dfs, dssp_kd_rd_con_dfs)
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if len(merging_cols_m4) > 1 and all(x in merging_cols_m4 for x in ['chain', 'position', 'wild_type']) :
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print('\nlength of merging_cols_m4 is > 1 and has mixed dtype'
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, '\nDropping chain and wild_type from dssp_kd_rd_con_dfs')
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dssp_kd_rd_con_dfs = dssp_kd_rd_con_dfs.drop(['chain', 'wild_type'], axis = 1)
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print('\nDetecting merging cols again')
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merging_cols_m4 = detect_common_cols(mcsm_foldx_deepddg_dfs, dssp_kd_rd_con_dfs)
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print('\nMerging cols length:', len(merging_cols_m4))
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if len(merging_cols_m4) == 1 and 'position' in merging_cols_m4:
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print('\nMerging column length == 1. Using it'
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, '\nMerging column name:', merging_cols_m4)
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else:
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sys.exit('\nFAIL: to merge mcsm_foldx_deepddg and dssp_kd_rd_con_dfs. Please check')
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combined_7dfs = pd.merge(mcsm_foldx_deepddg_dfs
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, dssp_kd_rd_con_dfs
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, on = merging_cols_m4
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, how = "inner")
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combined_df_expected_cols = ncols_deepddg_merge + ncols_m3 - len(merging_cols_m4)
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if len(combined_7dfs.columns) == len(mcsm_foldx_deepddg_dfs.columns) + len(dssp_kd_rd_con_dfs.columns) - len(merging_cols_m4):
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print('\n Seven dfs successfully combined:'
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, '\nmcsm-lig', '\nfoldx', '\ndeepddg'
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, '\ndssp', '\nkd', '\nrd'
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, '\nconsurf')
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else:
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print('\nSomething went wrong with the seventh merge'
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, '\nPlease check individual merges')
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sys.exit()
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#%%============================================================================
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print('======================================='
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, '\nSeventh merge: Sixth merge + snap2_df_f'
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, '\ncombined_7dfs + snap_df_f'
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, '\n=======================================')
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# Check: whether logic effects anything else!
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# Detect merging cols
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merging_cols_m4_v2 = detect_common_cols(combined_7dfs, snap2_df_f)
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if len(merging_cols_m4_v2) == 1:
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print ('\nProceeding with merging SNAP2 df with 7 already combined dfs')
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else:
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print('\nHalting Seventh merge. Please check merging cols'
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,'\nLength of merging_cols_m4_v2 is:', len(merging_cols_m4_v2))
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sys.exit()
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combined_df = pd.merge(combined_7dfs
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, snap2_df_f
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, on = merging_cols_m4_v2
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, how = "inner")
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combined_df_expected_cols = len(combined_7dfs.columns) + len(snap2_df_f.columns) - len(merging_cols_m4_v2)
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#%% Dimension checks for combined_df:
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# CHECK whether logic effects anything else!
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if not gene == "embB":
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print("\nGene is:", gene)
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if len(combined_df) == len(mcsm_df) and len(combined_df.columns) == combined_df_expected_cols:
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print('PASS: successfully combined 5 dfs'
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print('PASS: successfully combined 8 dfs'
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, '\nNo. of rows combined_df:', len(combined_df)
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, '\nNo. of cols combined_df:', len(combined_df.columns))
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else:
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#sys.exit('FAIL: check individual df merges')
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print("\nGene is:", gene
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, "\ncombined_df length:", len(combined_df)
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, "\nmcsm_df_length:", len(mcsm_df)
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)
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if len(combined_df.columns) == combined_df_expected_cols:
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print('PASS: successfully combined 5 dfs'
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, '\nNo. of rows combined_df:', len(combined_df)
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, '\nNo. of cols combined_df:', len(combined_df.columns))
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else:
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sys.exit('FAIL: check individual merges')
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elif len(combined_df) == len(foldx_df) and len(combined_df.columns) == combined_df_expected_cols:
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print('PASS: successfully combined 8 dfs'
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, '\nNo. of rows combined_df:', len(combined_df)
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, '\nNo. of cols combined_df:', len(combined_df.columns))
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else:
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sys.exit('FAIL: check individual merges for seventh merge')
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print('\nResult of Fourth merge:', combined_df.shape
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print('\nResult of Seventh merge:', combined_df.shape
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, '\n===================================================================')
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combined_df[merging_cols_m4].apply(len)
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combined_df[merging_cols_m4].apply(len) == len(combined_df)
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# should be TRUE and length match mcsm_df len
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combined_df[merging_cols_m4_v2].apply(len)
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combined_df[merging_cols_m4_v2].apply(len) == len(combined_df)
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#%%============================================================================
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# Format the combined df columns
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combined_df_colnames = combined_df.columns
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# check redundant columns
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combined_df['chain'].equals(combined_df['chain_id'])
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combined_df['wild_type'].equals(combined_df['wild_type_kd']) # has nan
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combined_df['wild_type'].equals(combined_df['wild_type_kd']) # should not have nan
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combined_df['wild_type'].equals(combined_df['wild_type_dssp'])
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# sanity check
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foo = combined_df[['wild_type', 'wild_type_kd', 'wt_3letter_caps', 'wt_aa_3lower', 'mut_aa_3lower']]
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# Drop cols
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cols_to_drop = ['chain_id', 'wild_type_kd', 'wild_type_dssp', 'wt_3letter_caps']
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cols_to_drop = ['chain_id', 'wild_type_kd', 'wild_type_dssp'
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, 'wt_3letter_caps', 'wt_aa_3lower', 'mut_aa_3lower']
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print('\nDropping:', len(cols_to_drop), 'cols to get clean_df for futher combining'
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, '\nncols before dropping:', len(combined_df.columns)
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, '\nDropped column names:', cols_to_drop)
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combined_df_clean = combined_df.drop(cols_to_drop, axis = 1)
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combined_df_clean.columns
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print('\nncols after dropping:', len(combined_df_clean.columns))
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combined_df_clean_colnames = combined_df_clean.columns
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del(foo)
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#%%============================================================================
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#---------------------
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@ -966,7 +1067,7 @@ if len(combined_stab_afor) == len(combined_df_clean) and len(combined_stab_afor.
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else:
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sys.exit('\nFAIL: check individual df merges')
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print('\n\nResult of Fifth merge:', combined_stab_afor.shape
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print('\n\nResult of Seventh merge:', combined_stab_afor.shape
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, '\n===================================================================')
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combined_stab_afor[merging_cols_m5].apply(len)
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@ -1029,8 +1130,7 @@ dfs_merged = reduce(lambda left,right: pd.merge(left
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, right
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||||
, on = ['mutationinformation']
|
||||
#, how = 'inner')
|
||||
, how = join_type)
|
||||
, dfs_list)
|
||||
, how = join_type) , dfs_list)
|
||||
# drop excess columns
|
||||
drop_cols = detect_common_cols(dfs_merged, combined_stab_afor)
|
||||
drop_cols.remove('mutationinformation')
|
||||
|
|
@ -1038,8 +1138,8 @@ drop_cols.remove('mutationinformation')
|
|||
dfs_merged_clean = dfs_merged.drop(drop_cols, axis = 1)
|
||||
merging_cols_m6 = detect_common_cols(dfs_merged_clean, combined_stab_afor)
|
||||
|
||||
len(dfs_merged_clean.columns)
|
||||
len(combined_stab_afor.columns)
|
||||
len(dfs_merged_clean.columns)
|
||||
|
||||
combined_all_params = pd.merge(combined_stab_afor
|
||||
, dfs_merged_clean
|
||||
|
|
@ -1064,12 +1164,47 @@ if combined_all_params.columns.str.contains(r'_x$|_y$', regex = True).any():
|
|||
, '\nDropping these before writing file')
|
||||
extra_cols_to_drop = list(combined_all_params.columns.str.extract(r'(.*_x$|.*_y$)', expand = True).dropna()[0])
|
||||
print('\nTotal cols:', len(combined_all_params.columns)
|
||||
,'\nDropping:', len(extra_cols_to_drop), 'columns')
|
||||
,'\nDropping:', len(extra_cols_to_drop), 'columns'
|
||||
, '\nThese are:', extra_cols_to_drop)
|
||||
#extra_cols_to_drop = ['chain_x', 'chain_y']
|
||||
combined_all_params = combined_all_params.drop(extra_cols_to_drop, axis = 1)
|
||||
else:
|
||||
print('\nNo duplicate column names detected, just writing file'
|
||||
, '\nTotal cols:', len(combined_all_params.columns) )
|
||||
#%%==============================================================================
|
||||
# Some final formatting for combined_all_params df
|
||||
|
||||
#---------------------------------------
|
||||
# Add: Map 1 letter
|
||||
# code to 3Upper for Mutant aa
|
||||
#---------------------------------------
|
||||
# initialise a sub dict that is lookup dict for
|
||||
# 3-LETTER aa code to 1-LETTER aa code
|
||||
|
||||
lookup_dict = dict()
|
||||
for k, v in oneletter_aa_dict.items():
|
||||
lookup_dict[k] = v['three_letter_code_upper']
|
||||
mut = combined_all_params['mutant_type'].squeeze()
|
||||
combined_all_params['mut_3upper'] = mut.map(lookup_dict)
|
||||
else:
|
||||
print('\nFailed to add 3 letter upper code')
|
||||
|
||||
# Add offset value
|
||||
combined_all_params['seq_offset4pdb'] = offset_val
|
||||
|
||||
# Drop extra columns
|
||||
redundant_colsL = ['mutationinformation_snap2'
|
||||
, 'wt_upper'
|
||||
, 'mut_upper'
|
||||
, 'pdb_file']
|
||||
|
||||
combined_all_params_f = combined_all_params.drop(redundant_colsL
|
||||
, axis = 1
|
||||
, errors = 'ignore')
|
||||
|
||||
# Add pdb_file name at the end
|
||||
combined_all_params_f['pdb_file'] = gene_pdb_f
|
||||
|
||||
#%%============================================================================
|
||||
#---------------------
|
||||
# Output 3: write csv
|
||||
|
|
@ -1078,9 +1213,9 @@ print('\nWriting file: all params')
|
|||
print('\nOutput 3 filename:', outfile_comb
|
||||
, '\n===================================================================\n')
|
||||
|
||||
combined_all_params.to_csv(outfile_comb, index = False)
|
||||
combined_all_params_f.to_csv(outfile_comb, index = False)
|
||||
|
||||
print('\nFinished writing file:'
|
||||
, '\nNo. of rows:', combined_all_params.shape[0]
|
||||
, '\nNo. of cols:', combined_all_params.shape[1])
|
||||
, '\nNo. of rows:', combined_all_params_f.shape[0]
|
||||
, '\nNo. of cols:', combined_all_params_f.shape[1])
|
||||
#%% end of script
|
||||
|
|
@ -5,8 +5,6 @@
|
|||
#########################################################
|
||||
# working dir and loading libraries
|
||||
getwd()
|
||||
#setwd("~/git/LSHTM_analysis/scripts/plotting")
|
||||
|
||||
source("/home/tanu/git/LSHTM_analysis/scripts/plotting/Header_TT.R")
|
||||
|
||||
#********************
|
||||
|
|
@ -53,7 +51,7 @@ my_df_u = pd_df[[2]] # this forms one of the input for combining_dfs_plotting()
|
|||
max_ang <- round(max(my_df_u[LigDist_colname]))
|
||||
min_ang <- round(min(my_df_u[LigDist_colname]))
|
||||
|
||||
cat("\nLigand distance cut off, colname:", LigDist_colname
|
||||
cat("\nLigand distance colname:", LigDist_colname
|
||||
, "\nThe max distance", gene, "structure df" , ":", max_ang, "\u212b"
|
||||
, "\nThe min distance", gene, "structure df" , ":", min_ang, "\u212b")
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue