saving work after running combining_dfs.py
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7 changed files with 136 additions and 92 deletions
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@ -119,7 +119,7 @@ gene_list_normal = ["pnca", "katg", "rpob", "alr"]
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#FIXME: for gid, this should be SRY as this is the drug...please check!!!!
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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_SAM.csv'
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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'
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@ -140,7 +140,7 @@ deepddg_df = pd.read_csv(infile_deepddg, sep = ',')
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in_filename_dssp = gene.lower() + '_dssp.csv'
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infile_dssp = outdir + in_filename_dssp
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dssp_df = pd.read_csv(infile_dssp, sep = ',')
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dssp_df_raw = pd.read_csv(infile_dssp, sep = ',')
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in_filename_kd = gene.lower() + '_kd.csv'
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infile_kd = outdir + in_filename_kd
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@ -164,10 +164,13 @@ infilename_dynamut2 = gene.lower() + '_dynamut2_norm.csv'
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infile_dynamut2 = outdir + 'dynamut_results/dynamut2/' + infilename_dynamut2
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dynamut2_df = pd.read_csv(infile_dynamut2, sep = ',')
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#------------------------------------------------------------
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infilename_mcsm_f_snps = gene.lower() + '_mcsm_formatted_snps.csv'
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infile_mcsm_f_snps = outdir + infilename_mcsm_f_snps
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mcsm_f_snps = pd.read_csv(infile_mcsm_f_snps, sep = ',', names = ['mutationinformation'], header = None)
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#------------------------------------------------------------------------------
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# ONLY:for gene pnca and gid: End logic should pick this up!
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geneL_dy_na = ["pnca", "gid"]
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#if gene.lower() == "pnca" or "gid" :
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geneL_dy_na = ['gid']
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if gene.lower() in geneL_dy_na :
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print("\nGene:", gene.lower()
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, "\nReading Dynamut and mCSM_na files")
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@ -179,27 +182,28 @@ if gene.lower() in geneL_dy_na :
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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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# FIXME: ppi2, not extracted as expected for alr
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# TODO: get mcsm_ppi2 data for alr
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# ONLY:for gene embb and alr: End logic should pick this up!
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geneL_ppi2 = ["embb", "alr"]
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geneL_ppi2 = ['embb', 'alr']
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#if gene.lower() == "embb" or "alr":
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if gene.lower() in "embb" or "alr":
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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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mcsm_ppi2_df = pd.read_csv(infile_mcsm_ppi2, sep = ',')
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#--------------------------------------------------------------
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infilename_mcsm_f_snps = gene.lower() + '_mcsm_formatted_snps.csv'
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infile_mcsm_f_snps = outdir + infilename_mcsm_f_snps
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mcsm_f_snps = pd.read_csv(infile_mcsm_f_snps, sep = ',', names = ['mutationinformation'], header = None)
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if gene.lower() == "embb":
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sel_chain = "B"
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else:
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sel_chain = "A"
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#------------------------------------------------------------------------------
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#=======
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# output
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#=======
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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('Output filename:', outfile_comb
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print('\nOutput filename:', outfile_comb
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, '\n===================================================================')
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# end of variable assignment for input and output files
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#%%############################################################################
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#=====================
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@ -233,7 +237,7 @@ len(foldx_df.loc[foldx_df['ddg_foldx'] < 0])
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foldx_scale = lambda x : x/abs(foldx_min) if x < 0 else (x/foldx_max if x >= 0 else 'failed')
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foldx_df['foldx_scaled'] = foldx_df['ddg_foldx'].apply(foldx_scale)
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print('Raw foldx scores:\n', foldx_df['ddg_foldx']
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print('\nRaw foldx scores:\n', foldx_df['ddg_foldx']
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, '\n---------------------------------------------------------------'
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, '\nScaled foldx scores:\n', foldx_df['foldx_scaled'])
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@ -276,9 +280,42 @@ 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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#--------------------------
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# check if >1 chain
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#--------------------------
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deepddg_df.loc[:,'chain_id'].value_counts()
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if len(deepddg_df.loc[:,'chain_id'].value_counts()) > 1:
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print("\nChains detected: >1"
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, "\nGene:", gene
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, "\nChains:", deepddg_df.loc[:,'chain_id'].value_counts().index)
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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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# Check for duplicates
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#--------------------------
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if len(deepddg_df['mutationinformation'].duplicated().value_counts())> 1:
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print("\nFAIL: Duplicates detected in DeepDDG infile"
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, "\nNo. of duplicates:"
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, deepddg_df['mutationinformation'].duplicated().value_counts()[1]
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, "\nformat deepDDG infile before proceeding")
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sys.exit()
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else:
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print("\nPASS: No duplicates detected in DeepDDG infile")
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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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#--------------------------
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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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#-------------------------
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# scale Deepddg values
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#-------------------------
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@ -290,7 +327,7 @@ deepddg_max = deepddg_df['deepddg'].max()
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deepddg_scale = lambda x : x/abs(deepddg_min) if x < 0 else (x/deepddg_max if x >= 0 else 'failed')
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deepddg_df['deepddg_scaled'] = deepddg_df['deepddg'].apply(deepddg_scale)
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print('Raw deepddg scores:\n', deepddg_df['deepddg']
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print('\nRaw deepddg scores:\n', deepddg_df['deepddg']
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, '\n---------------------------------------------------------------'
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, '\nScaled deepddg scores:\n', deepddg_df['deepddg_scaled'])
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@ -310,8 +347,8 @@ else:
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print('\nFAIL: deepddg values scaled numbers MISmatch'
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, '\nExpected number:', deepddg_pos
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, '\nGot:', deepddg_pos2
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, '\n======================================================')
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, '\n======================================================')
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#--------------------------
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# Deepddg outcome category
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#--------------------------
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@ -327,49 +364,15 @@ else:
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, '\nGot:', doc[0]
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, '\n======================================================')
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sys.exit()
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#--------------------------
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# check if >1 chain
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#--------------------------
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deepddg_df.loc[:,'chain_id'].value_counts()
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if len(deepddg_df.loc[:,'chain_id'].value_counts()) > 1:
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print("\nChains detected: >1"
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, "\nGene:", gene
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, "\nChains:", deepddg_df.loc[:,'chain_id'].value_counts().index)
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#--------------------------
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# FIXME: This needs to happen BEFORE scaling as it will vary
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# subset chain
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#--------------------------
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if gene.lower() == "embb":
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sel_chain = "B"
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else:
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sel_chain = "A"
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deepddg_df = deepddg_df[deepddg_df['chain_id'] == sel_chain]
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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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# Check for duplicates
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#--------------------------
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if len(deepddg_df['mutationinformation'].duplicated().value_counts())> 1:
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print("\nFAIL: Duplicates detected in DeepDDG infile"
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, "\nNo. of duplicates:"
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, deepddg_df['mutationinformation'].duplicated().value_counts()[1]
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, "\nformat deepDDG infile before proceeding")
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sys.exit()
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else:
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print("\nPASS: No duplicates detected in DeepDDG infile")
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#--------------------------
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# Drop chain id col as other targets don't have itCheck for duplicates
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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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#%%=============================================================================
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#%%============================================================================
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# Now merges begin
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#%%=============================================================================
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print('==================================='
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, '\nFirst merge: mcsm + foldx'
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, '\n===================================')
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@ -399,10 +402,11 @@ 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
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# get the empty cells to be full of meaningful info
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#%% for embB and any other targets where 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 ("NAs detected in mcsm cols after merge")
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print ('\nNAs detected in mcsm cols after merge.'
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, '\nCleaning data before merging deepddg_df')
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##############################
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# Extract relevant col values
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@ -446,6 +450,8 @@ if mcsm_foldx_dfs.loc[:,'wild_type': 'mut_aa_3lower'].isnull().values.any():
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mcsm_foldx_dfs['wt_aa_3lower'] = wt.map(lookup_dict)
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mut = mcsm_foldx_dfs['mutant_type'].squeeze()
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mcsm_foldx_dfs['mut_aa_3lower'] = mut.map(lookup_dict)
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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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print('==================================='
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@ -464,14 +470,18 @@ 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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print('==================================='
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, '\Third merge: dssp + kd'
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, '\nThird merge: dssp + kd'
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, '\n===================================')
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dssp_df.shape
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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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@ -557,17 +567,19 @@ combined_df['wild_type'].equals(combined_df['wild_type_dssp'])
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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', 'wt_3letter_caps']
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combined_df_clean = combined_df.drop(cols_to_drop, axis = 1)
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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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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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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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@ -646,7 +658,7 @@ else:
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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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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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@ -661,7 +673,7 @@ print('\nFinished writing file:'
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#dfs_list = [dynamut_df, dynamut2_df, mcsm_na_df] # gid
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if gene.lower() == "pnca":
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dfs_list = [dynamut_df, dynamut2_df]
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dfs_list = [dynamut2_df]
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if gene.lower() == "gid":
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dfs_list = [dynamut_df, dynamut2_df, mcsm_na_df]
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if gene.lower() == "embb":
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