bring in embb stuff which was in the wrong branch

mcsm_ppi2/format_results_mcsm_ppi2.py

@@ -0,0 +1,159 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Aug 19 14:33:51 2020
+
+@author: tanu
+"""
+#%% load packages
+import os,sys
+homedir = os.path.expanduser('~')
+import subprocess
+import argparse
+import requests
+import re
+import time
+from bs4 import BeautifulSoup
+import pandas as pd
+import numpy as np
+from pandas.api.types import is_string_dtype
+from pandas.api.types import is_numeric_dtype
+
+sys.path.append(homedir + '/git/LSHTM_analysis/scripts')
+from reference_dict import up_3letter_aa_dict
+from reference_dict import oneletter_aa_dict
+
+#%%#####################################################################
+
+def format_mcsm_ppi2_output(mcsm_ppi2_output_csv):
+    """
+    @param mcsm_ppi2_output_csv: file containing mcsm_ppi2_results for all muts 
+     which is the result of combining all mcsm_ppi2 batch results, and using
+     bash scripts to combine all the batch results into one file. 
+     Formatting df to a pandas df and output as csv.
+     @type string
+
+     @return (not true) formatted csv for mcsm_ppi2 output
+     @type pandas df
+
+     """
+    #############
+    # Read file
+    #############
+    mcsm_ppi2_data_raw  = pd.read_csv(mcsm_ppi2_output_csv, sep = ',')  
+    
+    # strip white space from both ends in all columns
+    mcsm_ppi2_data = mcsm_ppi2_data_raw.apply(lambda x: x.str.strip() if x.dtype == 'object' else x)
+
+    dforig_shape = mcsm_ppi2_data.shape
+    print('dimensions of input file:', dforig_shape) 
+
+    #############
+    # Map 3 letter 
+    # code to one
+    #############
+    # 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 up_3letter_aa_dict.items():
+        lookup_dict[k] = v['one_letter_code']
+        wt = mcsm_ppi2_data['wild-type'].squeeze() # converts to a series that map works on
+        mcsm_ppi2_data['w_type'] = wt.map(lookup_dict)   
+        mut = mcsm_ppi2_data['mutant'].squeeze()
+        mcsm_ppi2_data['m_type'] = mut.map(lookup_dict)
+    
+    # #############
+    # # CHECK
+    # # Map 1 letter 
+    # # code to 3Upper
+    # #############
+    # # 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']
+    #     wt = mcsm_ppi2_data['w_type'].squeeze() #converts to a series that map works on
+    #     mcsm_ppi2_data['WILD'] = wt.map(lookup_dict)   
+    #     mut = mcsm_ppi2_data['m_type'].squeeze()
+    #     mcsm_ppi2_data['MUT'] = mut.map(lookup_dict)
+    
+    # # check
+    # mcsm_ppi2_data['wild-type'].equals(mcsm_ppi2_data['WILD'])
+    # mcsm_ppi2_data['mutant'].equals(mcsm_ppi2_data['MUT'])
+#%%============================================================================    
+    #############
+    # rename cols
+    #############
+    # format colnames: all lowercase and consistent colnames
+    mcsm_ppi2_data.columns
+    print('Assigning meaningful colnames'
+            , '\n=======================================================')
+    
+    my_colnames_dict = {'chain': 'chain'
+        , 'wild-type': 'wt_upper'
+        , 'res-number': 'position'
+        , 'mutant': 'mut_upper'
+        , 'distance-to-interface': 'interface_dist'
+        , 'mcsm-ppi2-prediction': 'mcsm_ppi2_affinity'
+        , 'affinity': 'mcsm_ppi2_outcome'
+        , 'w_type': 'wild_type' # one letter amino acid code
+        , 'm_type': 'mutant_type' # one letter amino acid code  
+} 
+
+    mcsm_ppi2_data.rename(columns = my_colnames_dict, inplace = True)
+    mcsm_ppi2_data.columns
+
+    #############
+    # create mutationinformation column
+    #############    
+    #mcsm_ppi2_data['mutationinformation'] = mcsm_ppi2_data['wild_type'] + mcsm_ppi2_data.position.map(str) + mcsm_ppi2_data['mutant_type']
+    mcsm_ppi2_data['mutationinformation'] = mcsm_ppi2_data.loc[:,'wild_type'] + mcsm_ppi2_data.loc[:,'position'].astype(int).apply(str) + mcsm_ppi2_data.loc[:,'mutant_type']
+
+#%%=====================================================================
+    #########################
+    # scale mcsm_ppi2 values
+    #########################
+    # Rescale values in mcsm_ppi2_affinity col b/w -1 and 1 so negative numbers
+    # stay neg and pos numbers stay positive
+    mcsm_ppi2_min = mcsm_ppi2_data['mcsm_ppi2_affinity'].min() 
+    mcsm_ppi2_max = mcsm_ppi2_data['mcsm_ppi2_affinity'].max() 
+    
+    mcsm_ppi2_scale = lambda x : x/abs(mcsm_ppi2_min) if x < 0 else (x/mcsm_ppi2_max if x >= 0 else 'failed')
+    
+    mcsm_ppi2_data['mcsm_ppi2_scaled'] = mcsm_ppi2_data['mcsm_ppi2_affinity'].apply(mcsm_ppi2_scale)
+    print('Raw mcsm_ppi2 scores:\n', mcsm_ppi2_data['mcsm_ppi2_affinity']
+        , '\n---------------------------------------------------------------'
+        , '\nScaled mcsm_ppi2 scores:\n', mcsm_ppi2_data['mcsm_ppi2_scaled'])
+    
+    c = mcsm_ppi2_data[mcsm_ppi2_data['mcsm_ppi2_affinity']>=0].count()
+    mcsm_ppi2_pos = c.get(key = 'mcsm_ppi2_affinity')
+    
+    c2 = mcsm_ppi2_data[mcsm_ppi2_data['mcsm_ppi2_scaled']>=0].count()
+    mcsm_ppi2_pos2 = c2.get(key = 'mcsm_ppi2_scaled')
+    
+    if mcsm_ppi2_pos == mcsm_ppi2_pos2:
+        print('\nPASS: Affinity values scaled correctly')
+    else:
+        print('\nFAIL: Affinity values scaled numbers MISmatch'
+              , '\nExpected number:', mcsm_ppi2_pos
+              , '\nGot:', mcsm_ppi2_pos2
+              , '\n======================================================')
+
+#%%=====================================================================
+    #############
+    # reorder columns
+    #############
+    mcsm_ppi2_data.columns
+    mcsm_ppi2_dataf = mcsm_ppi2_data[['mutationinformation'
+                                , 'mcsm_ppi2_affinity'
+                                , 'mcsm_ppi2_scaled'
+                                , 'mcsm_ppi2_outcome'
+                                , 'interface_dist'
+                                , 'wild_type'
+                                , 'position'
+                                , 'mutant_type'
+                                , 'wt_upper'
+                                , 'mut_upper'
+                                , 'chain']]
+    return(mcsm_ppi2_dataf)
+#%%##################################################################### 

mcsm_ppi2/run_format_results_mcsm_ppi2.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Feb 12 12:15:26 2021
+
+@author: tanu
+"""
+#%% load packages
+import sys, os
+homedir = os.path.expanduser('~')
+#sys.path.append(homedir + '/git/LSHTM_analysis/mcsm_ppi2')
+
+from format_results_mcsm_ppi2 import *
+########################################################################
+# TODO: add cmd line args
+#%% command line args
+arg_parser = argparse.ArgumentParser()
+arg_parser.add_argument('-d', '--drug'      , help = 'drug name (case sensitive)', default = None)
+arg_parser.add_argument('-g', '--gene'      , help = 'gene name (case sensitive)', default = None)
+arg_parser.add_argument('--datadir'         , help = 'Data Directory. By default, it assmumes homedir + git/Data')
+arg_parser.add_argument('-i', '--input_dir' , help = 'Input dir containing pdb files. By default, it assmumes homedir + <drug> + input')
+arg_parser.add_argument('-o', '--output_dir', help = 'Output dir for results. By default, it assmes homedir + <drug> + output')
+#arg_parser.add_argument('--mkdir_name'      , help = 'Output dir for processed results. This will be created if it does not exist') 
+arg_parser.add_argument('-m', '--make_dirs' , help = 'Make dir for input and output', action='store_true')
+
+arg_parser.add_argument('--debug'           , action = 'store_true' , help = 'Debug Mode')
+
+args = arg_parser.parse_args()
+#%%============================================================================ 
+# variable assignment: input and output paths & filenames
+drug         = args.drug
+gene         = args.gene
+datadir      = args.datadir
+indir        = args.input_dir
+outdir       = args.output_dir
+#outdir_ppi2  = args.mkdir_name
+make_dirs    = args.make_dirs
+
+#=======
+# dirs
+#=======
+if not datadir:
+    datadir = homedir + '/git/Data/'
+    
+if not indir:
+    indir = datadir + drug + '/input/'
+    
+if not outdir:
+    outdir = datadir + drug + '/output/'
+
+#if not mkdir_name:
+#    outdir_ppi2 = outdir + 'mcsm_ppi2/'
+
+outdir_ppi2 = outdir + 'mcsm_ppi2/'
+
+# Input file
+infile_mcsm_ppi2 =  outdir_ppi2 +  gene.lower() + '_output_combined_clean.csv'
+
+# Formatted output file
+outfile_mcsm_ppi2_f = outdir_ppi2 + gene.lower() + '_complex_mcsm_ppi2_norm.csv'
+
+#==========================
+# CALL: format_results_mcsm_na() 
+# Data: gid+streptomycin
+#==========================
+print('Formatting results for:', infile_mcsm_ppi2)
+mcsm_ppi2_df_f = format_mcsm_ppi2_output(mcsm_ppi2_output_csv = infile_mcsm_ppi2)
+
+# writing file
+print('Writing formatted df to csv')
+mcsm_ppi2_df_f.to_csv(outfile_mcsm_ppi2_f, index = False)
+
+print('Finished writing file:'
+       , '\nFile:', outfile_mcsm_ppi2_f
+       , '\nExpected no. of rows:', len(mcsm_ppi2_df_f)
+       , '\nExpected no. of cols:', len(mcsm_ppi2_df_f.columns)
+       , '\n=============================================================')
+
+#%%#####################################################################

scripts/combining_dfs.py

@@ -34,6 +34,11 @@ Created on Tue Aug  6 12:56:03 2019
 # Output: single csv of all 8 dfs combined
 # useful link
 # https://stackoverflow.com/questions/23668427/pandas-three-way-joining-multiple-dataframes-on-columns
+
+#%% FIXME: let the script proceed even if files don't exist!
+# i.e example below
+# '/home/tanu/git/Data/ethambutol/output/dynamut_results/embb_complex_dynamut_norm.csv'
+
 #=======================================================================
 #%% load packages
 import sys, os
@@ -41,13 +46,14 @@ import pandas as pd
 from pandas import DataFrame
 import numpy as np
 import argparse
+from functools import reduce
 #=======================================================================
 #%% specify input and curr dir
 homedir = os.path.expanduser('~')
 
 # set working dir
 os.getcwd()
-os.chdir(homedir + '/git/LSHTM_analysis/scripts')
+#os.chdir(homedir + '/git/LSHTM_analysis/scripts')
 os.getcwd()
 
 # FIXME: local imports
@@ -92,19 +98,6 @@ outdir  = args.output_dir
 gene_match = gene + '_p.'
 print('mut pattern for gene', gene, ':',  gene_match)
 
-# !"Redundant, now that improvements have been made!
-# See section "REGEX"
-# nssnp_match = gene_match +'[A-Za-z]{3}[0-9]+[A-Za-z]{3}'
-# print('nsSNP for gene', gene, ':',  nssnp_match)
-
-# wt_regex = gene_match.lower()+'([A-Za-z]{3})'
-# print('wt regex:', wt_regex)
-
-# mut_regex = r'[0-9]+(\w{3})$'
-# print('mt regex:', mut_regex)
-
-# pos_regex = r'([0-9]+)'
-# print('position regex:', pos_regex)
 #%%=======================================================================
 #==============
 # directories
@@ -121,65 +114,263 @@ if not outdir:
 #=======
 # input
 #=======
-#in_filename_mcsm = gene.lower() + '_complex_mcsm_norm.csv' 
+gene_list_normal = ["pnca", "katg", "rpob", "alr"]
-in_filename_mcsm = gene.lower() + '_complex_mcsm_norm_SAM.csv' # gidb
-in_filename_foldx = gene.lower() + '_foldx.csv'
-in_filename_deepddg = gene.lower() + '_ni_deepddg.csv' # change to decent filename and put it in the correct dir
 
-in_filename_dssp = gene.lower() + '_dssp.csv'
+if gene.lower() == "gid":
-in_filename_kd = gene.lower() + '_kd.csv'
+    print("\nReading mCSM file for gene:", gene)
-in_filename_rd = gene.lower() + '_rd.csv'
+    in_filename_mcsm = gene.lower() + '_complex_mcsm_norm_SAM.csv'
+if gene.lower() == "embb":
+    print("\nReading mCSM file for gene:", gene)
+    in_filename_mcsm = gene.lower() + '_complex_mcsm_norm1.csv'
+if gene.lower() in gene_list_normal:
+   print("\nReading mCSM file for gene:", gene)
+   in_filename_mcsm = gene.lower() + '_complex_mcsm_norm.csv' 
+    
+infile_mcsm           = outdir + in_filename_mcsm
+mcsm_df               = pd.read_csv(infile_mcsm, sep = ',')
 
-#in_filename_snpinfo = 'ns' + gene.lower() + '_snp_info_f.csv' # gwas f info
+in_filename_foldx     = gene.lower() + '_foldx.csv'
-in_filename_afor = gene.lower() + '_af_or.csv'
+infile_foldx          = outdir + in_filename_foldx
-#in_filename_afor_kin = gene.lower() + '_af_or_kinship.csv'
+foldx_df              = pd.read_csv(infile_foldx , sep = ',')
 
-infile_mcsm = outdir + in_filename_mcsm
+in_filename_deepddg   = gene.lower() + '_ni_deepddg.csv' # change to decent filename and put it in the correct dir
-infile_foldx = outdir + in_filename_foldx
+infile_deepddg        = outdir + in_filename_deepddg
-infile_deepddg = outdir + in_filename_deepddg
+deepddg_df            = pd.read_csv(infile_deepddg, sep = ',')
 
-infile_dssp = outdir + in_filename_dssp
+in_filename_dssp      = gene.lower() + '_dssp.csv'
-infile_kd = outdir + in_filename_kd
+infile_dssp           = outdir + in_filename_dssp
-infile_rd = outdir + in_filename_rd
+dssp_df               = pd.read_csv(infile_dssp, sep = ',')
 
-#infile_snpinfo = outdir + '/' + in_filename_snpinfo 
+in_filename_kd        = gene.lower() + '_kd.csv'
-infile_afor = outdir + '/' + in_filename_afor
+infile_kd             = outdir + in_filename_kd
-#infile_afor_kin = outdir + '/' + in_filename_afor_kin
+kd_df                 = pd.read_csv(infile_kd, sep = ',')
 
-print('\nInput path:', indir
+in_filename_rd        = gene.lower() + '_rd.csv'
-      , '\nOutput path:', outdir, '\n'
+infile_rd             = outdir + in_filename_rd
-      , '\nInput filename mcsm:', infile_mcsm
+rd_df                 = pd.read_csv(infile_rd, sep = ',')
-      , '\nInput filename foldx:', infile_foldx, '\n'
+
-      , '\nInput filename deepddg', infile_deepddg , '\n'
+#in_filename_snpinfo   = 'ns' + gene.lower() + '_snp_info_f.csv' # gwas f info
-      , '\nInput filename dssp:', infile_dssp
+#infile_snpinfo        = outdir + in_filename_snpinfo 
-      , '\nInput filename kd:', infile_kd 
+
-      , '\nInput filename rd', infile_rd
+in_filename_afor      = gene.lower() + '_af_or.csv'
-     
+infile_afor           = outdir + in_filename_afor
-      #, '\nInput filename snp info:', infile_snpinfo, '\n'
+afor_df               = pd.read_csv(infile_afor, sep = ',') 
-      , '\nInput filename af or:', infile_afor
+
-      #, '\nInput filename afor kinship:', infile_afor_kin
+#in_filename_afor_kin  = gene.lower() + '_af_or_kinship.csv'
-      , '\n============================================================')
+#infile_afor_kin       = outdir + in_filename_afor_kin
+
+infilename_dynamut2    = gene.lower() + '_dynamut2_norm.csv'
+infile_dynamut2        = outdir + 'dynamut_results/dynamut2/' + infilename_dynamut2
+dynamut2_df            = pd.read_csv(infile_dynamut2, sep = ',')
+
+#------------------------------------------------------------
+# ONLY:for gene pnca and gid: End logic should pick this up!
+geneL_dy_na = ["pnca", "gid"]
+#if gene.lower() == "pnca" or "gid" :
+if gene.lower() in geneL_dy_na :
+    print("\nGene:", gene.lower()
+          , "\nReading Dynamut and mCSM_na files")
+    infilename_dynamut    = gene.lower() + '_dynamut_norm.csv' # gid
+    infile_dynamut        = outdir + 'dynamut_results/' + infilename_dynamut
+    dynamut_df            = pd.read_csv(infile_dynamut, sep = ',')
+    
+    infilename_mcsm_na    = gene.lower() + '_complex_mcsm_na_norm.csv' # gid
+    infile_mcsm_na        = outdir + 'mcsm_na_results/' + infilename_mcsm_na 
+    mcsm_na_df            = pd.read_csv(infile_mcsm_na, sep = ',')
+
+# ONLY:for gene embb and alr: End logic should pick this up!
+geneL_ppi2 = ["embb", "alr"]
+#if gene.lower() == "embb" or "alr":
+if gene.lower() in "embb" or "alr":
+    infilename_mcsm_ppi2   = gene.lower() + '_complex_mcsm_ppi2_norm.csv'
+    infile_mcsm_ppi2       = outdir + 'mcsm_ppi2/' + infilename_mcsm_ppi2
+    mcsm_ppi2_df           = pd.read_csv(infile_mcsm_ppi2, sep = ',')
+#--------------------------------------------------------------
+infilename_mcsm_f_snps = gene.lower() + '_mcsm_formatted_snps.csv'
+infile_mcsm_f_snps     = outdir + infilename_mcsm_f_snps
+mcsm_f_snps            = pd.read_csv(infile_mcsm_f_snps, sep = ',', names = ['mutationinformation'], header = None)
 
 #=======
 # output 
 #=======
 out_filename_comb = gene.lower() + '_all_params.csv'
-outfile_comb =  outdir + '/' + out_filename_comb
+outfile_comb =  outdir + out_filename_comb
 print('Output filename:', outfile_comb
       , '\n===================================================================')
-
-o_join = 'outer'
-l_join = 'left'
-r_join = 'right'
-i_join = 'inner'
-
 # end of variable assignment for input and output files
-#%%============================================================================  
+#%%############################################################################  
+#=====================
+# some preprocessing
+#=====================
+
+#===========
+# FoldX
+#===========
+foldx_df.shape
+
+#----------------------
+# scale foldx values
+#----------------------
+# rename ddg column to ddg_foldx
+foldx_df['ddg']
+foldx_df = foldx_df.rename(columns = {'ddg':'ddg_foldx'})   
+foldx_df['ddg_foldx']
+  
+# Rescale values in Foldx_change col b/w -1 and 1 so negative numbers
+# stay neg and pos numbers stay positive
+foldx_min = foldx_df['ddg_foldx'].min() 
+foldx_max = foldx_df['ddg_foldx'].max() 
+foldx_min
+foldx_max
+
+# quick check
+len(foldx_df.loc[foldx_df['ddg_foldx'] >= 0])
+len(foldx_df.loc[foldx_df['ddg_foldx'] < 0])
+
+foldx_scale = lambda x : x/abs(foldx_min) if x < 0 else (x/foldx_max if x >= 0 else 'failed')
+
+foldx_df['foldx_scaled'] = foldx_df['ddg_foldx'].apply(foldx_scale)
+print('Raw foldx scores:\n', foldx_df['ddg_foldx']
+    , '\n---------------------------------------------------------------'
+    , '\nScaled foldx scores:\n', foldx_df['foldx_scaled'])
+
+# additional check added
+fsmi = foldx_df['foldx_scaled'].min()
+fsma = foldx_df['foldx_scaled'].max()
+
+c = foldx_df[foldx_df['ddg_foldx']>=0].count()
+foldx_pos = c.get(key = 'ddg_foldx')
+
+c2 = foldx_df[foldx_df['foldx_scaled']>=0].count()
+foldx_pos2 = c2.get(key = 'foldx_scaled')
+
+if foldx_pos == foldx_pos2 and fsmi == -1 and fsma == 1:
+    print('\nPASS: Foldx values scaled correctly b/w -1 and 1')
+else:
+    print('\nFAIL: Foldx values scaled numbers MISmatch'
+          , '\nExpected number:', foldx_pos
+          , '\nGot:', foldx_pos2
+          , '\n======================================================')
+
+#-------------------------
+# foldx outcome category:
+# Remember, its inverse
+# +ve: Destabilising
+# -ve: Stabilising
+#--------------------------
+foldx_df['foldx_outcome'] = foldx_df['ddg_foldx'].apply(lambda x: 'Destabilising' if x >= 0 else 'Stabilising')
+foldx_df[foldx_df['ddg_foldx']>=0].count()
+foc = foldx_df['foldx_outcome'].value_counts()
+
+if foc['Destabilising'] == foldx_pos and foc['Destabilising'] == foldx_pos2:
+    print('\nPASS: Foldx outcome category created')
+else:
+    print('\nFAIL: Foldx outcome category could NOT be created'
+          , '\nExpected number:', foldx_pos
+          , '\nGot:', foc[0]
+          , '\n======================================================')
+    sys.exit()
+
+#=======================
+# Deepddg
+#=======================
+deepddg_df.shape
+
+#-------------------------
+# scale Deepddg values
+#-------------------------
+# Rescale values in deepddg_change col b/w -1 and 1 so negative numbers
+# stay neg and pos numbers stay positive
+deepddg_min = deepddg_df['deepddg'].min() 
+deepddg_max = deepddg_df['deepddg'].max() 
+
+deepddg_scale = lambda x : x/abs(deepddg_min) if x < 0 else (x/deepddg_max if x >= 0 else 'failed')
+
+deepddg_df['deepddg_scaled'] = deepddg_df['deepddg'].apply(deepddg_scale)
+print('Raw deepddg scores:\n', deepddg_df['deepddg']
+    , '\n---------------------------------------------------------------'
+    , '\nScaled deepddg scores:\n', deepddg_df['deepddg_scaled'])
+    
+# additional check added
+dsmi = deepddg_df['deepddg_scaled'].min()
+dsma = deepddg_df['deepddg_scaled'].max()
+
+c = deepddg_df[deepddg_df['deepddg']>=0].count()
+deepddg_pos = c.get(key = 'deepddg')
+
+c2 = deepddg_df[deepddg_df['deepddg_scaled']>=0].count()
+deepddg_pos2 = c2.get(key = 'deepddg_scaled')
+    
+if deepddg_pos == deepddg_pos2 and dsmi == -1 and dsma == 1:
+    print('\nPASS: deepddg values scaled correctly b/w -1 and 1')
+else:
+    print('\nFAIL: deepddg values scaled numbers MISmatch'
+          , '\nExpected number:', deepddg_pos
+          , '\nGot:', deepddg_pos2
+          , '\n======================================================')
+    
+#--------------------------
+# Deepddg outcome category
+#--------------------------
+deepddg_df['deepddg_outcome'] = deepddg_df['deepddg'].apply(lambda x: 'Stabilising' if x >= 0 else 'Destabilising')
+deepddg_df[deepddg_df['deepddg']>=0].count()
+doc = deepddg_df['deepddg_outcome'].value_counts()
+
+if doc['Stabilising'] == deepddg_pos and  doc['Stabilising'] == deepddg_pos2:
+    print('\nPASS: Deepddg outcome category created')
+else:
+    print('\nFAIL: Deepddg outcome category could NOT be created'
+          , '\nExpected number:', deepddg_pos
+          , '\nGot:', doc[0]
+          , '\n======================================================')
+    sys.exit()
+
+#--------------------------
+# check if >1 chain
+#--------------------------
+deepddg_df.loc[:,'chain_id'].value_counts()
+
+if len(deepddg_df.loc[:,'chain_id'].value_counts()) > 1:
+    print("\nChains detected: >1"
+          , "\nGene:", gene
+          , "\nChains:", deepddg_df.loc[:,'chain_id'].value_counts().index)
+    
+#--------------------------
+# subset chain
+#--------------------------
+if gene.lower() == "embb":
+    sel_chain = "B"
+else:
+    sel_chain = "A"
+    
+deepddg_df = deepddg_df[deepddg_df['chain_id'] == sel_chain]
+    
+#--------------------------
+# Check for duplicates
+#--------------------------
+if len(deepddg_df['mutationinformation'].duplicated().value_counts())> 1:
+    print("\nFAIL: Duplicates detected in DeepDDG infile"
+          , "\nNo. of duplicates:"
+          , deepddg_df['mutationinformation'].duplicated().value_counts()[1]
+          , "\nformat deepDDG infile before proceeding")
+    sys.exit()
+else:
+    print("\nPASS: No duplicates detected in DeepDDG infile")
+
+#--------------------------
+# Drop chain id col as other targets don't have itCheck for duplicates
+#--------------------------
+col_to_drop = ['chain_id']
+deepddg_df = deepddg_df.drop(col_to_drop, axis = 1)
+
+#%%=============================================================================
+# Now merges begin
+#%%=============================================================================
 print('==================================='
       , '\nFirst merge: mcsm + foldx'
       , '\n===================================')
 
-mcsm_df =  pd.read_csv(infile_mcsm, sep = ',')
+mcsm_df.shape
 
 # add 3 lowercase aa code for wt and mutant
 get_aa_3lower(df = mcsm_df
@@ -189,42 +380,100 @@ get_aa_3lower(df = mcsm_df
               , col_mut = 'mut_aa_3lower')
 
 #mcsm_df.columns = mcsm_df.columns.str.lower()
-foldx_df =  pd.read_csv(infile_foldx , sep = ',')
+# foldx_df.shape
 
-#mcsm_foldx_dfs = combine_dfs_with_checks(mcsm_df, foldx_df, my_join = o_join)
+#mcsm_foldx_dfs = combine_dfs_with_checks(mcsm_df, foldx_df, my_join = "outer")
 merging_cols_m1  = detect_common_cols(mcsm_df, foldx_df)
-mcsm_foldx_dfs = pd.merge(mcsm_df, foldx_df, on = merging_cols_m1,  how =  o_join)
+mcsm_foldx_dfs   = pd.merge(mcsm_df
+                          , foldx_df
+                          , on = merging_cols_m1
+                          , how =  "outer")
 ncols_m1 = len(mcsm_foldx_dfs.columns)
 
 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)
+
+#%% for embB and any other targets where mCSM-lig hasn't run for 
+# get the empty cells to be full of meaningful info
+if mcsm_foldx_dfs.loc[:,'wild_type': 'mut_aa_3lower'].isnull().values.any():  
+    print ("NAs detected in mcsm cols after merge")
+    
+    ##############################
+    # Extract relevant col values 
+    # code to one
+    ##############################
+
+    # wt_reg = r'(^[A-Z]{1})'
+    # print('wild_type:', wt_reg)
+
+    # mut_reg = r'[0-9]+(\w{1})$'
+    # print('mut type:', mut_reg)
+    mcsm_foldx_dfs['wild_type']     = mcsm_foldx_dfs.loc[:,'mutationinformation'].str.extract(r'(^[A-Z]{1})')
+    mcsm_foldx_dfs['position']      = mcsm_foldx_dfs.loc[:,'mutationinformation'].str.extract(r'([0-9]+)')
+    mcsm_foldx_dfs['mutant_type']   = mcsm_foldx_dfs.loc[:,'mutationinformation'].str.extract(r'[0-9]+([A-Z]{1})$')
+    
+    # BEWARE: Bit of logic trap i.e if nan comes first
+    # in chain column, then nan will be populated!
+    #df['foo'] = df['chain'].unique()[0]
+    mcsm_foldx_dfs['chain'] = np.where(mcsm_foldx_dfs[['chain']].isnull().all(axis=1)
+                                        , mcsm_foldx_dfs['chain'].unique()[0]
+                                        , mcsm_foldx_dfs['chain'])
+    
+    mcsm_foldx_dfs['ligand_id'] = np.where(mcsm_foldx_dfs[['ligand_id']].isnull().all(axis=1)
+                                        , mcsm_foldx_dfs['ligand_id'].unique()[0]
+                                        , mcsm_foldx_dfs['ligand_id'])
+    #--------------------------------------------------------------------------
+    
+    mcsm_foldx_dfs['wild_pos']       = mcsm_foldx_dfs.loc[:,'wild_type'] + mcsm_foldx_dfs.loc[:,'position'].astype(int).apply(str)
+    mcsm_foldx_dfs['wild_chain_pos'] = mcsm_foldx_dfs.loc[:,'wild_type'] + mcsm_foldx_dfs.loc[:,'chain'] +  mcsm_foldx_dfs.loc[:,'position'].astype(int).apply(str)
+    
+    #############
+    # Map 1 letter 
+    # code to 3Upper
+    #############
+    # 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_lower']
+        wt = mcsm_foldx_dfs['wild_type'].squeeze() # converts to a series that map works on
+        mcsm_foldx_dfs['wt_aa_3lower'] = wt.map(lookup_dict)   
+        mut = mcsm_foldx_dfs['mutant_type'].squeeze()
+        mcsm_foldx_dfs['mut_aa_3lower'] = mut.map(lookup_dict)
+    
 #%%
 print('==================================='
       , '\nSecond merge: mcsm_foldx_dfs + deepddg'
       , '\n===================================')
 
-deepddg_df =  pd.read_csv(infile_deepddg, sep = ',')
-deepddg_df.columns
-
 # merge with mcsm_foldx_dfs and deepddg_df
-mcsm_foldx_deepddg_dfs = pd.merge(mcsm_foldx_dfs, deepddg_df, on = 'mutationinformation',  how = l_join)
+mcsm_foldx_deepddg_dfs = pd.merge(mcsm_foldx_dfs
+                                  , deepddg_df
+                                  , on = 'mutationinformation'
+                                  , how = "left")
 mcsm_foldx_deepddg_dfs['deepddg_outcome'].value_counts()
 
 ncols_deepddg_merge = len(mcsm_foldx_deepddg_dfs.columns)
+
+mcsm_foldx_deepddg_dfs['position'] = mcsm_foldx_deepddg_dfs['position'].astype('int64')
+
 #%%============================================================================
 print('==================================='
       , '\Third merge: dssp + kd'
       , '\n===================================')
 
-dssp_df = pd.read_csv(infile_dssp, sep = ',')
+dssp_df.shape
-kd_df = pd.read_csv(infile_kd, sep = ',')
+kd_df.shape
-rd_df = pd.read_csv(infile_rd, sep = ',')
+rd_df.shape
 
-#dssp_kd_dfs = combine_dfs_with_checks(dssp_df, kd_df, my_join = o_join)
+#dssp_kd_dfs = combine_dfs_with_checks(dssp_df, kd_df, my_join = "outer")
 merging_cols_m2 = detect_common_cols(dssp_df, kd_df)
-dssp_kd_dfs = pd.merge(dssp_df, kd_df, on = merging_cols_m2,  how = o_join)
+dssp_kd_dfs = pd.merge(dssp_df
+                       , kd_df
+                       , on = merging_cols_m2
+                       , how = "outer")
 
 print('\n\nResult of third merge:', dssp_kd_dfs.shape
       , '\n===================================================================')
@@ -233,10 +482,12 @@ print('==================================='
       , '\nFourth merge: third merge + rd_df' 
       , '\ndssp_kd_dfs + rd_df'
       , '\n===================================')
-#dssp_kd_rd_dfs = combine_dfs_with_checks(dssp_kd_dfs, rd_df, my_join = o_join)
+#dssp_kd_rd_dfs = combine_dfs_with_checks(dssp_kd_dfs, rd_df, my_join = "outer")
 merging_cols_m3 = detect_common_cols(dssp_kd_dfs,  rd_df)
-dssp_kd_rd_dfs = pd.merge(dssp_kd_dfs, rd_df, on = merging_cols_m3
+dssp_kd_rd_dfs = pd.merge(dssp_kd_dfs
-                          , how = o_join)
+                          , rd_df
+                          , on = merging_cols_m3
+                          , how = "outer")
 
 ncols_m3 = len(dssp_kd_rd_dfs.columns)
 
@@ -249,24 +500,41 @@ print('======================================='
       , '\nFifth merge: Second merge + fourth merge'
       , '\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)
+
+#combined_df = combine_dfs_with_checks(mcsm_foldx_dfs, dssp_kd_rd_dfs, my_join = "inner")
 #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 = pd.merge(mcsm_foldx_dfs, dssp_kd_rd_dfs, on = merging_cols_m4, how  = "inner")
 #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 = pd.merge(mcsm_foldx_deepddg_dfs
+                       , dssp_kd_rd_dfs
+                       , on = merging_cols_m4
+                       , how  = "inner")
 
 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:
+# FIXME: check logic, doesn't effect anything else!
-    print('PASS: successfully combined 5 dfs'
+if not gene == "embB":
-          , '\nNo. of rows combined_df:', len(combined_df)
+    print("\nGene is:", gene)
-          , '\nNo. of cols combined_df:', len(combined_df.columns))   
+    if len(combined_df) == len(mcsm_df) and len(combined_df.columns) == combined_df_expected_cols:
-else:
+        print('PASS: successfully combined 5 dfs'
-    sys.exit('FAIL: check individual df merges')
+              , '\nNo. of rows combined_df:', len(combined_df)
-
+              , '\nNo. of cols combined_df:', len(combined_df.columns))   
+    else:
+        #sys.exit('FAIL: check individual df merges')
+        print("\nGene is:", gene
+              , "\ncombined_df length:", len(combined_df)
+              , "\nmcsm_df_length:", len(mcsm_df)
+              )
+        if len(combined_df.columns) == combined_df_expected_cols:
+            print('PASS: successfully combined 5 dfs'
+                  , '\nNo. of rows combined_df:', len(combined_df)
+                  , '\nNo. of cols combined_df:', len(combined_df.columns))
+        else:
+            sys.exit('FAIL: check individual merges')        
+            
 print('\nResult of Fourth merge:', combined_df.shape
       , '\n===================================================================')
 
@@ -281,7 +549,7 @@ 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
+# sanity check
 foo = combined_df[['wild_type', 'wild_type_kd', 'wt_3letter_caps', 'wt_aa_3lower', 'mut_aa_3lower']] 
 
 # Drop cols
@@ -308,8 +576,8 @@ print('\n======================================='
       , '\ncombined_df_clean + afor_df '
       , '\n=======================================')
 
-afor_df = pd.read_csv(infile_afor, sep = ',') 
 afor_cols = afor_df.columns
+afor_df.shape
 
 # create a mapping from the gwas mutation column i.e <gene_match>_abcXXXrst
 #----------------------
@@ -335,7 +603,11 @@ afor_df = afor_df.drop(['position'], axis = 1)
 afor_cols = afor_df.columns
 
 # merge 
-combined_stab_afor = pd.merge(combined_df_clean, afor_df, on = merging_cols_m5, how  = l_join)
+combined_stab_afor = pd.merge(combined_df_clean
+                              , afor_df
+                              , on = merging_cols_m5
+                              , how  = "left")
+
 comb_afor_df_cols = combined_stab_afor.columns
 
 comb_afor_expected_cols = len(combined_df_clean.columns) + len(afor_df.columns) - len(merging_cols_m5)
@@ -347,20 +619,28 @@ if len(combined_stab_afor) == len(combined_df_clean) and len(combined_stab_afor.
 else:
     sys.exit('\nFAIL: check individual df merges')
 
-print('\n\nResult of Fourth merge:', combined_stab_afor.shape
+print('\n\nResult of Fifth merge:', combined_stab_afor.shape
       , '\n===================================================================')
 
 combined_stab_afor[merging_cols_m5].apply(len)
 combined_stab_afor[merging_cols_m5].apply(len) == len(combined_stab_afor)
 
-if len(combined_stab_afor) -  combined_stab_afor['mutation'].isna().sum() == len(afor_df):
+if (len(combined_stab_afor) - combined_stab_afor['mutation'].isna().sum()) == len(afor_df):
-     print('\nPASS: Merge successful for af and or'
+     print('\nPASS: Merge successful for af and or with matched numbers')
-          , '\nNo. of nsSNPs with valid ORs: ', len(afor_df))
-else:
-    sys.exit('\nFAIL: merge unsuccessful for af and or')
 
+if len(combined_stab_afor) - combined_stab_afor['mutation'].isna().sum() == len(afor_df)-len(afor_df[~afor_df['mutation'].isin(combined_stab_afor['mutation'])]):
+    print("\nMismatched numbers, OR df has extra snps not found in mcsm df"
+          , "\nNo. of nsSNPs with valid ORs:", len(afor_df)
+          , "\nNo. of mcsm nsSNPs: ", len(combined_df_clean) 
+          , "\nNo. of OR nsSNPs not in mCSM df:"
+          , len(afor_df[~afor_df['mutation'].isin(combined_stab_afor['mutation'])])
+          , "\nWriting these mutations to file:")
+    orsnps_notmcsm = afor_df[~afor_df['mutation'].isin(combined_stab_afor['mutation'])]
+else:
+    sys.exit('\nFAIL: merge unsuccessful for af and or')    
+    
 #%%============================================================================
-# Output columns
+# Output columns: when dynamut, dynamut2 and others weren't being combined
 out_filename_comb_afor = gene.lower() + '_comb_afor.csv'
 outfile_comb_afor =  outdir + '/' + out_filename_comb_afor
 print('Output filename:', outfile_comb_afor
@@ -372,4 +652,61 @@ combined_stab_afor.to_csv(outfile_comb_afor, index = False)
 print('\nFinished writing file:'
       , '\nNo. of rows:', combined_stab_afor.shape[0]
       , '\nNo. of cols:', combined_stab_afor.shape[1])
-#%% end of script
+#%%============================================================================
+# combine dynamut, dynamut2, and mcsm_na
+#dfs_list = [dynamut_df, dynamut2_df, mcsm_na_df] # gid
+
+if gene.lower() == "pnca":
+    dfs_list = [dynamut_df, dynamut2_df]
+if gene.lower() == "gid":
+    dfs_list = [dynamut_df, dynamut2_df, mcsm_na_df]
+if gene.lower() == "embb":
+    dfs_list = [dynamut2_df, mcsm_ppi2_df]
+if gene.lower() == "katg":
+    dfs_list = [dynamut2_df]
+if gene.lower() == "rpob":
+    dfs_list = [dynamut2_df]
+if gene.lower() == "alr":
+    dfs_list = [dynamut2_df, mcsm_ppi2_df]
+
+dfs_merged = reduce(lambda  left,right: pd.merge(left
+                                                , right
+                                                , on = ['mutationinformation']
+                                                , how = 'inner')
+                   , dfs_list)
+# drop excess columns
+drop_cols = detect_common_cols(dfs_merged, combined_stab_afor)
+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)
+
+combined_all_params = pd.merge(combined_stab_afor
+                               , dfs_merged_clean
+                               , on = merging_cols_m6
+                               , how  = "inner")
+
+expected_ncols = len(dfs_merged_clean.columns) + len(combined_stab_afor.columns) - len(merging_cols_m6)
+expected_nrows = len(combined_stab_afor)
+
+if len(combined_all_params.columns) == expected_ncols and len(combined_all_params) == expected_nrows:
+    print('\nPASS: All dfs combined')
+else:
+    print('\nFAIL:lengths mismatch'
+          , '\nExpected ncols:', expected_ncols
+          , '\nGot:', len(dfs_merged_clean.columns) 
+          , '\nExpected nrows:', expected_nrows
+          , '\nGot:', len(dfs_merged_clean) )
+    
+#%% Done for gid on 10/09/2021
+# write csv
+print('Writing file: all params')
+combined_all_params.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])
+#%% end of script

scripts/data_extraction.py

@@ -70,7 +70,6 @@ arg_parser.add_argument('-m', '--make_dirs', help = 'Make dir for input and outp
 
 arg_parser.add_argument('--debug', action ='store_true', help = 'Debug Mode')
 
-
 args = arg_parser.parse_args()
 #=======================================================================
 #%% variable assignment: input and output paths & filenames
@@ -81,9 +80,6 @@ indir   = args.input_dir
 outdir  = args.output_dir
 make_dirs  = args.make_dirs
 
-#drug = 'streptomycin'
-#gene = 'gid'
-
 #%% input and output dirs and files
 #=======
 # dirs
@@ -1373,4 +1369,4 @@ if dr_muts.isin(other_muts).sum() & other_muts.isin(dr_muts).sum() > 0:
 print(u'\u2698' * 50,
       '\nEnd of script: Data extraction and writing files'
       '\n' + u'\u2698' * 50 )
-#%% end of script
+#%% end of script

scripts/deepddg_format.py

@@ -0,0 +1,149 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+'''
+Created on Tue Aug  6 12:56:03 2019
+
+@author: tanu
+'''
+#=======================================================================
+# Task: format deep ddg df to allow easy merging
+
+# Input:  2 dfs
+#1) <gene>.lower()'_mcsm_formatted_snps.csv'
+#2) <gene>.lower()_complex_ddg_results.csv'
+#=======================================================================
+#%% load packages
+import sys, os
+import pandas as pd
+from pandas import DataFrame
+import numpy as np
+#from varname import nameof
+import argparse
+#=======================================================================
+#%% specify input and curr dir
+homedir = os.path.expanduser('~')
+
+# set working dir
+os.getcwd()
+os.chdir(homedir + '/git/LSHTM_analysis/scripts')
+os.getcwd()
+#=======================================================================#%% command line args: case sensitive
+arg_parser = argparse.ArgumentParser()
+arg_parser.add_argument('-d', '--drug', help = 'drug name', default = '')
+arg_parser.add_argument('-g', '--gene', help = 'gene name', default = '') 
+
+arg_parser.add_argument('--datadir', help = 'Data Directory. By default, it assmumes homedir + git/Data')
+arg_parser.add_argument('-i', '--input_dir', help = 'Input dir containing pdb files. By default, it assmumes homedir + <drug> + input')
+arg_parser.add_argument('-o', '--output_dir', help = 'Output dir for results. By default, it assmes homedir + <drug> + output')
+
+arg_parser.add_argument('--debug', action ='store_true', help = 'Debug Mode')
+
+args = arg_parser.parse_args()
+#=======================================================================
+#%% variable assignment: input and output 
+drug    = args.drug
+gene    = args.gene
+datadir = args.datadir
+indir   = args.input_dir
+outdir  = args.output_dir
+#%%=======================================================================
+#==============
+# directories
+#==============
+if not datadir:
+    datadir = homedir + '/git/Data/'
+    
+if not indir:
+    indir = datadir + drug + '/input/'
+    
+if not outdir:
+    outdir = datadir + drug + '/output/'
+
+#=======
+# input
+#=======
+in_filename_mcsm_snps = gene.lower() + '_mcsm_formatted_snps.csv'
+infile_mcsm_snps = outdir + in_filename_mcsm_snps
+
+in_filename_deepddg = gene.lower() + '_complex_ddg_results.csv' 
+infile_deepddg = outdir + 'deep_ddg/' + in_filename_deepddg
+
+print('\nInput path:', indir
+      , '\nOutput path:', outdir, '\n'
+      , '\nInput filename mcsm snps', infile_mcsm_snps , '\n'
+      , '\nInput filename deepddg', infile_deepddg , '\n'
+      , '\n============================================================')
+
+#=======
+# output 
+#=======
+#out_filename_deepddg = gene.lower() + '_ni_deepddg.txt'
+out_filename_deepddg = gene.lower() + '_ni_deepddg.csv'
+outfile_deepddg_f =  outdir + out_filename_deepddg
+
+print('Output filename:', outfile_deepddg_f
+        , '\n===================================================================')
+# end of variable assignment for input and output files
+#%%============================================================================  
+print('==================================='
+      , '\nmcsm muts'
+      , '\n===================================')
+
+mcsm_muts_df =  pd.read_csv(infile_mcsm_snps , header = None, sep = ',', names = ['mutationinformation'])
+mcsm_muts_df.columns
+
+#%%============================================================================  
+print('==================================='
+      , '\nDeep ddg'
+      , '\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.columns
+deepddg_df['mutationinformation'] = deepddg_df['wild_type_deepddg'] + deepddg_df['position'].map(str) + deepddg_df['mutant_type_deepddg']
+deepddg_df.columns
+
+# 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()
+
+# should be identical in count ot Destabilising and stabilising respectively
+len(deepddg_df.loc[deepddg_df['deepddg'] < 0])
+len(deepddg_df.loc[deepddg_df['deepddg'] >= 0])
+               
+#----------------------------------------------
+# drop extra columns to allow clean merging
+#----------------------------------------------
+#deepddg_short_df = deepddg_df.drop(['chain_id', 'wild_type_deepddg', 'position', 'mutant_type_deepddg'], axis = 1)
+
+#----------------------------------------------
+# embb (where gene-target has > 1 chain)
+# include chain else the numbering will be messed up!
+#----------------------------------------------
+deepddg_short_df = deepddg_df.drop(['wild_type_deepddg', 'position', 'mutant_type_deepddg'], axis = 1)
+
+# rearrange columns
+deepddg_short_df.columns
+deepddg_short_df = deepddg_short_df[["chain_id", "mutationinformation", "deepddg", "deepddg_outcome"]]
+
+#%% combine with mcsm snps 
+deepddg_mcsm_muts_dfs = pd.merge(deepddg_short_df
+                                 , mcsm_muts_df
+                                 , on = 'mutationinformation'
+                                 , how = 'right')
+deepddg_mcsm_muts_dfs ['deepddg_outcome'].value_counts()
+
+#%%============================================================================
+# write csv
+print('Writing file: formatted deepddg and only mcsm muts')
+deepddg_mcsm_muts_dfs.to_csv(outfile_deepddg_f, index = False)
+print('\nFinished writing file:'
+      , '\nNo. of rows:', deepddg_mcsm_muts_dfs.shape[0]
+      , '\nNo. of cols:', deepddg_mcsm_muts_dfs.shape[1])
+#%% end of script

scripts/rd_df.py

@@ -45,8 +45,6 @@ arg_parser.add_argument('--debug', action='store_true', help = 'Debug Mode')
 args = arg_parser.parse_args()
 #=======================================================================
 #%% variable assignment: input and output 
-#drug = 'pyrazinamide'
-#gene = 'pncA'
 drug = args.drug
 gene = args.gene
 gene_match = gene + '_p.'