thorough checking and updates for final running of all gene targets
This commit is contained in:
parent
b66cf31219
commit
bffa3c376c
1 changed files with 137 additions and 90 deletions
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@ -8,7 +8,7 @@ Created on Tue Aug 6 12:56:03 2019
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#=======================================================================
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# Task: combining all dfs to a single one
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# Input: 8 dfs
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# Input: 12/13/14 dfs
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#1) <gene>.lower()'_complex_mcsm_norm.csv'
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#2) <gene>.lower()_foldx.csv'
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#3) <gene>.lower()_dssp.csv'
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@ -16,20 +16,16 @@ Created on Tue Aug 6 12:56:03 2019
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#5) <gene>.lower()_rd.csv'
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#6) 'ns' + <gene>.lower()_snp_info.csv'
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#7) <gene>.lower()_af_or.csv'
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#8) <gene>.lower() _af_or_kinship.csv
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#8) <gene>.lower() _af_or_kinship.csv (ONLY for pncA, but omitted for the final run)
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#9) <gene>.lower()'_dynamut2.csv'
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#10) <gene>.lower()'_dynamut.csv'
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#11) <gene>.lower()'_mcsm_na.csv'
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#12) <gene>.lower()'_mcsm_ppi2.csv'
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#13) <gene>.lower()'_consurf.csv'
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#14) <gene>.lower()'_snap2.csv'
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# combining order
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#Merge1 = 1 + 2
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#Merge2 = 3 + 4
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#Merge3 = Merge2 + 5
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#Merge4 = Merge1 + Merge3
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#Merge5 = 6 + 7
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#Merge6 = Merge5 + 8
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#Merge7 = Merge4 + Merge6
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# Output: single csv of all 8 dfs combined
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# useful link
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@ -53,10 +49,10 @@ homedir = os.path.expanduser('~')
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# set working dir
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os.getcwd()
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os.chdir(homedir + '/git/LSHTM_analysis/scripts')
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#os.chdir(homedir + '/git/LSHTM_analysis/scripts')
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sys.path.append(homedir + '/git/LSHTM_analysis/scripts')
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os.getcwd()
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# FIXME: local imports
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#from combining import combine_dfs_with_checks
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from combining_FIXME import detect_common_cols
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from reference_dict import oneletter_aa_dict
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@ -119,6 +115,7 @@ gene_list_normal = ['pnca', 'katg', 'rpob', 'alr']
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if gene.lower() == "gid":
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print("\nReading mCSM file for gene:", gene)
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in_filename_mcsm = gene.lower() + '_complex_mcsm_norm_SRY.csv' # was incorrectly SAM previously
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if gene.lower() == "embb":
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print("\nReading mCSM file for gene:", gene)
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#in_filename_mcsm = gene.lower() + '_complex_mcsm_norm1.csv' #798
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@ -183,14 +180,11 @@ infile_snap2 = outdir + 'snap2/'+ infilename_snap2
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snap2_df = pd.read_csv(infile_snap2, sep = ',')
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#------------------------------------------------------------------------------
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# ONLY:for gene pnca and gid: End logic should pick this up!
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# ONLY: for gene 'gid' and 'rpob': End logic should pick this up!
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geneL_na = ['gid', 'rpob']
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if gene.lower() in geneL_na:
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print("\nGene:", gene.lower()
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, "\nReading mCSM_na files")
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# infilename_dynamut = gene.lower() + '_dynamut_norm.csv' # gid
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# infile_dynamut = outdir + 'dynamut_results/' + infilename_dynamut
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# dynamut_df = pd.read_csv(infile_dynamut, sep = ',')
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infilename_mcsm_na = gene.lower() + '_complex_mcsm_na_norm.csv' # gid
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infile_mcsm_na = outdir + 'mcsm_na_results/' + infilename_mcsm_na
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@ -199,18 +193,13 @@ if gene.lower() in geneL_na:
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geneL_dy = ['gid']
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if gene.lower() in geneL_dy:
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print("\nGene:", gene.lower()
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, "\nReading Dynamut and mCSM_na files")
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, "\nReading Dynamut files")
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infilename_dynamut = gene.lower() + '_dynamut_norm.csv' # gid
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infile_dynamut = outdir + 'dynamut_results/' + infilename_dynamut
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dynamut_df = pd.read_csv(infile_dynamut, sep = ',')
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# infilename_mcsm_na = gene.lower() + '_complex_mcsm_na_norm.csv' # gid
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# infile_mcsm_na = outdir + 'mcsm_na_results/' + infilename_mcsm_na
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# mcsm_na_df = pd.read_csv(infile_mcsm_na, sep = ',')
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# ONLY:for gene embb and alr and katg: End logic should pick this up!
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geneL_ppi2 = ['embb', 'alr']
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#if gene.lower() == "embb" or "alr":
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# ONLY: for genes 'alr', 'embb', 'katg' and 'rpob': End logic should pick this up!
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geneL_ppi2 = ['alr', 'embb', 'katg', 'rpob']
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if gene.lower() in geneL_ppi2:
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infilename_mcsm_ppi2 = gene.lower() + '_complex_mcsm_ppi2_norm.csv'
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infile_mcsm_ppi2 = outdir + 'mcsm_ppi2/' + infilename_mcsm_ppi2
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@ -224,10 +213,17 @@ else:
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#=======
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# output
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#=======
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# outfile 3
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out_filename_comb = gene.lower() + '_all_params.csv'
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outfile_comb = outdir + out_filename_comb
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print('\nOutput filename:', outfile_comb
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, '\n===================================================================')
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# outfile 2
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out_filename_comb_afor = gene.lower() + '_comb_afor.csv'
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outfile_comb_afor = outdir + out_filename_comb_afor
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# outfile 1
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out_filename_stab_struc = gene.lower() + '_comb_stab_struc_params.csv'
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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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@ -235,6 +231,22 @@ print('\nOutput filename:', outfile_comb
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# some preprocessing
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#=====================
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#===========
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# KD
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#===========
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kd_df.shape
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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 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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#===========
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# FoldX
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#===========
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@ -305,7 +317,6 @@ else:
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#=======================
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# Deepddg
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# TODO: RERUN 'gid'
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#=======================
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deepddg_df.shape
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@ -324,7 +335,8 @@ print('\nSelecting chain:', sel_chain, 'for gene:', gene)
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deepddg_df = deepddg_df[deepddg_df['chain_id'] == sel_chain]
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#--------------------------
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# Drop chain id col as other targets don't have it.Check for duplicates
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# Drop chain_id col as other
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# targets don't have it.
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#--------------------------
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col_to_drop = ['chain_id']
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deepddg_df = deepddg_df.drop(col_to_drop, axis = 1)
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@ -374,12 +386,38 @@ else:
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, '\nGot:', deepddg_pos2
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, '\n======================================================')
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if deepddg_df['deepddg_scaled'].min() == -1 and deepddg_df['deepddg_scaled'].max() == 1:
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print('\nPASS: Deepddg data is scaled between -1 and 1',
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'\nproceeding with merge')
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#--------------------------
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# Deepddg outcome category
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#--------------------------
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deepddg_df['deepddg_outcome'] = deepddg_df['deepddg'].apply(lambda x: 'Stabilising' if x >= 0 else 'Destabilising')
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deepddg_df[deepddg_df['deepddg']>=0].count()
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doc = deepddg_df['deepddg_outcome'].value_counts()
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if 'deepddg_outcome' not in deepddg_df.columns:
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print('\nCreating column: deepddg_outcome')
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deepddg_df['deepddg_outcome'] = deepddg_df['deepddg'].apply(lambda x: 'Stabilising' if x >= 0 else 'Destabilising')
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deepddg_df[deepddg_df['deepddg']>=0].count()
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doc = deepddg_df['deepddg_outcome'].value_counts()
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print(doc)
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else:
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print('\nColumn exists: deepddg_outcome')
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t1 = deepddg_df['deepddg_outcome'].value_counts()
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deepddg_df['deepddg_outcome2'] = deepddg_df['deepddg'].apply(lambda x: 'Stabilising' if x >= 0 else 'Destabilising')
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t2 = deepddg_df['deepddg_outcome2'].value_counts()
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print('\n', t1, '\n', t2)
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#--------------------------
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# Drop deepddg_outcome2 col
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#--------------------------
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col_to_drop2 = ['deepddg_outcome2']
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deepddg_df = deepddg_df.drop(col_to_drop2, axis = 1)
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if all(t1 == t2):
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print('\nPASS: Deepddg_outcome category checked!')
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doc = deepddg_df['deepddg_outcome'].value_counts()
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else:
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print('\nMISmatch in deepddg_outcome counts'
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, '\n:', t1
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, '\n:', t2)
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if doc['Stabilising'] == deepddg_pos and doc['Stabilising'] == deepddg_pos2:
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print('\nPASS: Deepddg outcome category created')
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@ -390,18 +428,11 @@ else:
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, '\n======================================================')
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sys.exit()
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if deepddg_df['deepddg_scaled'].min() == -1 and deepddg_df['deepddg_scaled'].max() == 1:
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print('\nPASS: Deepddg data is scaled between -1 and 1',
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'\nproceeding with merge')
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#=======================
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# Consurf
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#=======================
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consurf_df.shape
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# drop row 0: as it contains no value but hangover text
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consurf_df = consurf_df.drop(index=0)
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#----------------------
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# rename colums
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#----------------------
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@ -419,8 +450,8 @@ if gene.lower() in geneL_consurf:
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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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@ -443,7 +474,7 @@ consurf_df = consurf_df.rename(columns={'SEQ' : 'wild_type'
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, 'MSADATA' : 'consurf_msa_data'
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, 'RESIDUEVARIETY' : 'consurf_aa_variety'})
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# quick check
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if len(consurf_df) == len(rd_df):
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if len(consurf_df) == len(kd_df):
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print('\nPASS: length of consurf df is as expected'
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, '\nProceeding to format consurf df')
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else:
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@ -458,6 +489,7 @@ consurf_df['consurf_colour'] = consurf_df['consurf_colour_str'].str.extract(r'(\
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consurf_df['consurf_colour'] = consurf_df['consurf_colour'].astype(int)
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consurf_df['consurf_colour_rev'] = consurf_df['consurf_colour_str'].str.replace(r'.\*','0')
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# non struc position are assigned a *, replacing that with a 0 so its all integer
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consurf_df['consurf_colour_rev'] = consurf_df['consurf_colour_rev'].astype(int)
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consurf_df['consurf_ci_upper'] = consurf_df['consurf_ci'].str.extract(r'(.*):')
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@ -468,10 +500,10 @@ consurf_df['consurf_ci_lower'] = consurf_df['consurf_ci_lower'].astype(float)
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#consurf_df['wt_3upper_f'] = consurf_df['wt_3upper'].str.extract(r'^\w{3}(\d+.*)')
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#consurf_df['wt_3upper_f']
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consurf_df['wt_3upper'] = consurf_df['wt_3upper'].str.replace(r'(\d+:.*)', '')
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consurf_df['chain'] = consurf_df['wt_3upper'].str.extract(r':(.*)')
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consurf_df['wt_3upper'] = consurf_df['wt_3upper'].str.replace(r'(\d+:.*)', '')
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#-------------------------
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# scale consurf values
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#-------------------------
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@ -517,8 +549,11 @@ consurf_df.columns
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#---------------------------
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# select columns
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# (and also determine order)
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# this removes redundant cols:
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# consurf_colour_str
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# consurf_ci
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#---------------------------
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consurf_df_f = consurf_df[['position'
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consurf_col_order = ['position'
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, 'wild_type'
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, 'chain'
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, 'wt_3upper'
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@ -530,7 +565,18 @@ consurf_df_f = consurf_df[['position'
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, 'consurf_ci_lower'
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, 'consurf_ci_colour'
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, 'consurf_msa_data'
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, 'consurf_aa_variety']]
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, 'consurf_aa_variety']
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consurf_df_f = consurf_df[consurf_col_order]
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# CHECK: whether a general rule or a gene specific rule!
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if consurf_df_f['chain'].isna().sum() > 0:
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print('\nNaN detected in column chain for consurf df')
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#if gene.lower() == 'embb':
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print('\nFurther consurf df processing for gene:', gene.lower())
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print('\nDropping Nan from column name chain')
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consurf_df_f = consurf_df_f[consurf_df_f['chain'].notna()]
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#=======================
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# SNAP2
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@ -610,10 +656,12 @@ else:
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, '\nGot:', snap2_pos2
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, '\n======================================================')
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#---------------------------
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#-------------------------------------
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# select columns
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# (and also determine order)
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#---------------------------
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# renumbering already done using
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# bash and corrected file is read in
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#-------------------------------------
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snap2_df.dtypes
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snap2_df.columns
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@ -718,7 +766,7 @@ if mcsm_foldx_dfs.loc[:,'wild_type': 'mut_aa_3lower'].isnull().values.any():
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else:
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print('\nNo NAs detected in mcsm_fold_dfs. Proceeding to merge deepddg_df')
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#%%
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#%%============================================================================
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print('==================================='
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, '\nSecond merge: mcsm_foldx_dfs + deepddg'
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, '\n===================================')
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@ -735,7 +783,7 @@ ncols_deepddg_merge = len(mcsm_foldx_deepddg_dfs.columns)
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mcsm_foldx_deepddg_dfs['position'] = mcsm_foldx_deepddg_dfs['position'].astype('int64')
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#%%============================================================================
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#FIXME: select df with 'chain' to allow corret dim merging!
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# Select df with 'chain' to allow corret dim merging!
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print('==================================='
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, '\nThird merge: dssp + kd'
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, '\n===================================')
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@ -755,7 +803,6 @@ dssp_kd_dfs = pd.merge(dssp_df
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#, how = "outer")
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, how = "inner")
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print('\n\nResult of third merge:', dssp_kd_dfs.shape
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, '\n===================================================================')
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#%%============================================================================
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@ -816,7 +863,7 @@ combined_df = pd.merge(mcsm_foldx_deepddg_dfs
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combined_df_expected_cols = ncols_deepddg_merge + ncols_m3 - len(merging_cols_m4)
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# FIXME: check logic, doesn't effect anything else!
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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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@ -859,16 +906,13 @@ combined_df_clean = combined_df.drop(cols_to_drop, axis = 1)
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combined_df_clean.columns
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del(foo)
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#%%============================================================================
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# Output columns
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out_filename_stab_struc = gene.lower() + '_comb_stab_struc_params.csv'
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outfile_stab_struc = outdir + out_filename_stab_struc
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print('Output filename:', outfile_stab_struc
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, '\n===================================================================')
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#---------------------
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# Output 1: write csv
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#---------------------
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print('\nWriting file: combined stability and structural parameters'
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, '\nOutput 1 filename:', outfile_stab_struc
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, '\n===================================================================\n')
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combined_df_clean
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# write csv
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print('\nWriting file: combined stability and structural parameters')
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combined_df_clean.to_csv(outfile_stab_struc, index = False)
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print('\nFinished writing file:'
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, '\nNo. of rows:', combined_df_clean.shape[0]
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@ -943,14 +987,14 @@ else:
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sys.exit('\nFAIL: merge unsuccessful for af and or')
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#%%============================================================================
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# Output columns: when dynamut, dynamut2 and others weren't being combined
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out_filename_comb_afor = gene.lower() + '_comb_afor.csv'
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outfile_comb_afor = outdir + out_filename_comb_afor
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print('Output filename:', outfile_comb_afor
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, '\n===================================================================')
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#---------------------
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# Output 2: write csv
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# when dynamut, dynamut2 and others weren't being combined
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#---------------------
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print('\nWriting file: combined stability and afor'
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, '\nOutput 2 filename:', outfile_comb_afor
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, '\n===================================================================\n')
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# write csv
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print('Writing file: combined stability and afor')
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combined_stab_afor.to_csv(outfile_comb_afor, index = False)
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print('\nFinished writing file:'
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, '\nNo. of rows:', combined_stab_afor.shape[0]
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@ -966,9 +1010,9 @@ if gene.lower() == "gid":
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if gene.lower() == "embb":
|
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dfs_list = [dynamut2_df, mcsm_ppi2_df]
|
||||
if gene.lower() == "katg":
|
||||
dfs_list = [dynamut2_df]
|
||||
dfs_list = [dynamut2_df, mcsm_ppi2_df]
|
||||
if gene.lower() == "rpob":
|
||||
dfs_list = [dynamut2_df, mcsm_na_df]
|
||||
dfs_list = [dynamut2_df, mcsm_na_df, mcsm_ppi2_df]
|
||||
if gene.lower() == "alr":
|
||||
dfs_list = [dynamut2_df, mcsm_ppi2_df]
|
||||
|
||||
|
|
@ -1014,7 +1058,6 @@ else:
|
|||
, '\nExpected nrows:', expected_nrows
|
||||
, '\nGot:', len(dfs_merged_clean) )
|
||||
|
||||
# FIXME: need to extract 'cols_to_drop' programatically
|
||||
# Drop cols
|
||||
if combined_all_params.columns.str.contains(r'_x$|_y$', regex = True).any():
|
||||
print('\nDuplicate column names detected...'
|
||||
|
|
@ -1027,10 +1070,14 @@ if combined_all_params.columns.str.contains(r'_x$|_y$', regex = True).any():
|
|||
else:
|
||||
print('\nNo duplicate column names detected, just writing file'
|
||||
, '\nTotal cols:', len(combined_all_params.columns) )
|
||||
#del(foo)
|
||||
#%% Done for gid on 10/09/2021
|
||||
# write csv
|
||||
print('Writing file: all params')
|
||||
#%%============================================================================
|
||||
#---------------------
|
||||
# Output 3: write csv
|
||||
#---------------------
|
||||
print('\nWriting file: all params')
|
||||
print('\nOutput 3 filename:', outfile_comb
|
||||
, '\n===================================================================\n')
|
||||
|
||||
combined_all_params.to_csv(outfile_comb, index = False)
|
||||
|
||||
print('\nFinished writing file:'
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue