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added deepddg data to combining_df.py

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Tanushree Tunstall 2021-06-21 11:53:56 +01:00
parent f79aea254e
commit 9534fc57d4
1 changed files with 53 additions and 8 deletions
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61
scripts/combining_dfs.py
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@ -131,7 +131,7 @@ in_filename_foldx = gene.lower() + '_foldx.csv'
in_filename_dssp = gene.lower() + '_dssp.csv'
in_filename_kd = gene.lower() + '_kd.csv'
in_filename_rd = gene.lower() + '_rd.csv'
#in_filename_deepddg = gene.lower() + '_complex_ddg_results.txt' # change to decent filename and put it in the correct dir
in_filename_deepddg = gene.lower() + '_complex_ddg_results.txt' # change to decent filename and put it in the correct dir
in_filename_snpinfo = 'ns' + gene.lower() + '_snp_info_f.csv' # gwas f info
in_filename_afor = gene.lower() + '_af_or.csv'
@ -143,7 +143,7 @@ infile_foldx = outdir + in_filename_foldx
infile_dssp = outdir + in_filename_dssp
infile_kd = outdir + in_filename_kd
infile_rd = outdir + in_filename_rd
#infile_deepddg = outdir + in_filename_deepddg
infile_deepddg = outdir + 'deep_ddg/' + in_filename_deepddg
infile_snpinfo = outdir + '/' + in_filename_snpinfo
infile_afor = outdir + '/' + in_filename_afor
@ -203,6 +203,37 @@ print('\n\nResult of first merge:', mcsm_foldx_dfs.shape
, '\n===================================================================')
mcsm_foldx_dfs[merging_cols_m1].apply(len)
mcsm_foldx_dfs[merging_cols_m1].apply(len) == len(mcsm_foldx_dfs)
#%%
print('==================================='
, '\nSecond merge: mcsm_foldx_dfs + deepddg'
, '\n===================================')
deepddg_df = pd.read_csv(infile_deepddg, sep = ' ')
deepddg_df.columns
deepddg_df.rename(columns = {'#chain' : 'chain_id'
, 'WT' : 'wild_type_deepddg'
, 'ResID' : 'position'
, 'Mut' : 'mutant_type_deepddg'}
, inplace = True)
deepddg_df['mutationinformation'] = deepddg_df['wild_type_deepddg'] + deepddg_df['position'].map(str) + deepddg_df['mutant_type_deepddg']
# add deepddg outcome column: <0--> Destabilising, >0 --> Stabilising
deepddg_df['deepddg_outcome'] = np.where(deepddg_df['deepddg'] < 0, 'Destabilising', 'Stabilising')
deepddg_df['deepddg_outcome'].value_counts()
# drop extra columns to allow clean merging
deepddg_short_df = deepddg_df.drop(['chain_id', 'wild_type_deepddg', 'position', 'mutant_type_deepddg'], axis = 1)
# rearrange columns
deepddg_short_df.columns
deepddg_short_df = deepddg_short_df[["mutationinformation", "deepddg", "deepddg_outcome"]]
mcsm_foldx_deepddg_dfs = pd.merge(mcsm_foldx_dfs, deepddg_short_df, on = 'mutationinformation', how = l_join)
mcsm_foldx_deepddg_dfs['deepddg_outcome'].value_counts()
ncols_deepddg_merge = len(mcsm_foldx_deepddg_dfs.columns)
#%%============================================================================
print('==================================='
, '\nSecond merge: dssp + kd'
@ -240,10 +271,15 @@ print('======================================='
, '\nmcsm_foldx_dfs + dssp_kd_rd_dfs'
, '\n=======================================')
#combined_df = combine_dfs_with_checks(mcsm_foldx_dfs, dssp_kd_rd_dfs, my_join = i_join)
merging_cols_m4 = detect_common_cols(mcsm_foldx_dfs, dssp_kd_rd_dfs)
combined_df = pd.merge(mcsm_foldx_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how = i_join)
#merging_cols_m4 = detect_common_cols(mcsm_foldx_dfs, dssp_kd_rd_dfs)
#combined_df = pd.merge(mcsm_foldx_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how = i_join)
#combined_df_expected_cols = ncols_m1 + ncols_m3 - len(merging_cols_m4)
combined_df_expected_cols = ncols_m1 + ncols_m3 - len(merging_cols_m4)
# with deepddg values
merging_cols_m4 = detect_common_cols(mcsm_foldx_deepddg_dfs, dssp_kd_rd_dfs)
combined_df = pd.merge(mcsm_foldx_deepddg_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how = i_join)
combined_df_expected_cols = ncols_deepddg_merge + ncols_m3 - len(merging_cols_m4)
if len(combined_df) == len(mcsm_df) and len(combined_df.columns) == combined_df_expected_cols:
print('PASS: successfully combined 5 dfs'
@ -256,15 +292,24 @@ print('\nResult of Fourth merge:', combined_df.shape
, '\n===================================================================')
combined_df[merging_cols_m4].apply(len)
combined_df[merging_cols_m4].apply(len) == len(combined_df)
#%%============================================================================
# Format the combined df columns
combined_df_colnames = combined_df.columns
#deepddg_df = pd.read_csv(infile_deepddg, sep = ' ')
# check redundant columns
combined_df['chain'].equals(combined_df['chain_id'])
combined_df['wild_type'].equals(combined_df['wild_type_kd']) # has nan
combined_df['wild_type'].equals(combined_df['wild_type_dssp'])
#sanity check
foo = combined_df[['wild_type', 'wild_type_kd', 'wt_3letter_caps', 'wt_aa_3lower', 'mut_aa_3lower']]
# Drop cols
cols_to_drop = ['chain_id', 'wild_type_kd', 'wild_type_dssp', 'wt_3letter_caps' ]
combined_df_clean = combined_df.drop(cols_to_drop, axis = 1)
del(foo)
#%%============================================================================
# Output columns
out_filename_stab_struc = gene.lower() + '_comb_stab_struc_params.csv'
outfile_stab_struc = outdir + '/' + out_filename_stab_struc
print('Output filename:', outfile_stab_struc