311 lines
15 KiB
Python
Executable file
311 lines
15 KiB
Python
Executable file
#!/usr/bin/env python3
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#=======================================================================
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#TASK:
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#=======================================================================
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#%% load packages
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import os,sys
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import subprocess
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import argparse
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#import requests
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import re
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#import time
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import pandas as pd
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from pandas.api.types import is_string_dtype
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from pandas.api.types import is_numeric_dtype
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import numpy as np
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#=======================================================================
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#%% specify input and curr dir
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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/mcsm')
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os.getcwd()
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#=======================================================================
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#%% variable assignment: input and output
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drug = 'pyrazinamide'
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gene = 'pncA'
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#drug = args.drug
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#gene = args.gene
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gene_match = gene + '_p.'
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#==========
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# dirs
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#==========
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datadir = homedir + '/' + 'git/Data'
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indir = datadir + '/' + drug + '/' + 'input'
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outdir = datadir + '/' + drug + '/' + 'output'
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#=======
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# input:
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#=======
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# 1) result_urls (from outdir)
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in_filename_mcsm_output = gene.lower() + '_mcsm_output.csv' #(outfile, from mcsm_results.py)
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infile_mcsm_output = outdir + '/' + in_filename_mcsm_output
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print('Input file:', infile_mcsm_output
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, '\n=============================================================')
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#=======
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# output
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#=======
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out_filename_mcsm_norm = gene.lower() + '_complex_mcsm_norm.csv'
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outfile_mcsm_norm = outdir + '/' + out_filename_mcsm_norm
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print('Output file:', out_filename_mcsm_norm
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, '\n=============================================================')
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#=======================================================================
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print('Reading input file')
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mcsm_data = pd.read_csv(infile_mcsm_output, sep = ',')
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mcsm_data.columns
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# PredAffLog = affinity_change_log
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# "DUETStability_Kcalpermol = DUET_change_kcalpermol
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dforig_shape = mcsm_data.shape
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print('dim of infile:', dforig_shape)
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#############
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# rename cols
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#############
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# format colnames: all lowercase, remove spaces and use '_' to join
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print('Assigning meaningful colnames i.e without spaces and hyphen and reflecting units'
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, '\n===================================================================')
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my_colnames_dict = {'Predicted Affinity Change': 'PredAffLog' # relevant info from this col will be extracted and the column discarded
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, 'Mutation information': 'mutation_information' # {wild_type}<position>{mutant_type}
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, 'Wild-type': 'wild_type' # one letter amino acid code
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, 'Position': 'position' # number
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, 'Mutant-type': 'mutant_type' # one letter amino acid code
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, 'Chain': 'chain' # single letter (caps)
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, 'Ligand ID': 'ligand_id' # 3-letter code
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, 'Distance to ligand': 'ligand_distance' # angstroms
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, 'DUET stability change': 'duet_stability_change'} # in kcal/mol
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mcsm_data.rename(columns = my_colnames_dict, inplace = True)
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#%%===========================================================================
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# populate mutation_information column:mcsm style muts {WT}<POS>{MUT}
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print('Populating column : mutation_information which is currently empty\n', mcsm_data['mutation_information'])
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mcsm_data['mutation_information'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str) + mcsm_data['mutant_type']
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print('checking after populating:\n', mcsm_data['mutation_information']
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, '\n===================================================================')
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# Remove spaces b/w pasted columns
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print('removing white space within column: \mutation_information')
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mcsm_data['mutation_information'] = mcsm_data['mutation_information'].str.replace(' ', '')
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print('Correctly formatted column: mutation_information\n', mcsm_data['mutation_information']
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, '\n===================================================================')
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#%% Remove whitespace from column
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#orig_dtypes = mcsm_data.dtypes
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#https://stackoverflow.com/questions/33788913/pythonic-efficient-way-to-strip-whitespace-from-every-pandas-data-frame-cell-tha/33789292
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#mcsm_data.columns = mcsm_data.columns.str.strip()
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#new_dtypes = mcsm_data.dtypes
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#%%===========================================================================
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# very important
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print('Sanity check:'
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, '\nChecking duplicate mutations')
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if mcsm_data['mutation_information'].duplicated().sum() == 0:
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print('PASS: No duplicate mutations detected (as expected)'
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, '\nDim of data:', mcsm_data.shape
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, '\n===============================================================')
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else:
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print('FAIL (but not fatal): Duplicate mutations detected'
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, '\nDim of df with duplicates:', mcsm_data.shape
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, 'Removing duplicate entries')
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mcsm_data = mcsm_data.drop_duplicates(['mutation_information'])
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print('Dim of data after removing duplicate muts:', mcsm_data.shape
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, '\n===============================================================')
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#%%===========================================================================
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# create duet_outcome column: classification based on DUET stability values
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print('Assigning col: duet_outcome based on DUET stability values')
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print('Sanity check:')
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# count positive values in the DUET column
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c = mcsm_data[mcsm_data['duet_stability_change']>=0].count()
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DUET_pos = c.get(key = 'duet_stability_change')
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# Assign category based on sign (+ve : Stabilising, -ve: Destabilising, Mind the spelling (British spelling))
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mcsm_data['duet_outcome'] = np.where(mcsm_data['duet_stability_change']>=0, 'Stabilising', 'Destabilising')
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mcsm_data['duet_outcome'].value_counts()
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if DUET_pos == mcsm_data['duet_outcome'].value_counts()['Stabilising']:
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print('PASS: DUET outcome assigned correctly')
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else:
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print('FAIL: DUET outcome assigned incorrectly'
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, '\nExpected no. of stabilising mutations:', DUET_pos
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, '\nGot no. of stabilising mutations', mcsm_data['duet_outcome'].value_counts()['Stabilising']
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, '\n===============================================================')
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#%%===========================================================================
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# Extract only the numeric part from col: ligand_distance
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# number: '-?\d+\.?\d*'
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mcsm_data['ligand_distance']
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print('extracting numeric part of col: ligand_distance')
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mcsm_data['ligand_distance'] = mcsm_data['ligand_distance'].str.extract('(\d+\.?\d*)')
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mcsm_data['ligand_distance']
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#%%===========================================================================
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# create ligand_outcome column: classification based on affinity change values
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# the numerical and categorical parts need to be extracted from column: PredAffLog
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# regex used
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# number: '-?\d+\.?\d*'
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# category: '\b(\w+ing)\b'
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print('Extracting numerical and categorical parts from the col: PredAffLog')
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print('to create two columns: ligand_affinity_change and ligand_outcome'
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, '\n===================================================================')
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# Extracting the predicted affinity change (numerical part)
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mcsm_data['ligand_affinity_change'] = mcsm_data['PredAffLog'].str.extract('(-?\d+\.?\d*)', expand = True)
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print(mcsm_data['ligand_affinity_change'])
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# Extracting the categorical part (Destabillizing and Stabilizing) using word boundary ('ing')
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#aff_regex = re.compile(r'\b(\w+ing)\b')
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mcsm_data['ligand_outcome']= mcsm_data['PredAffLog'].str.extract(r'(\b\w+ing\b)', expand = True)
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print(mcsm_data['ligand_outcome'])
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print(mcsm_data['ligand_outcome'].value_counts())
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# ensuring spellings are consistent
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american_spl = mcsm_data['ligand_outcome'].value_counts()
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print('Changing to Bristish spellings for col: ligand_outcome')
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mcsm_data['ligand_outcome'].replace({'Destabilizing': 'Destabilising', 'Stabilizing': 'Stabilising'}, inplace = True)
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print(mcsm_data['ligand_outcome'].value_counts())
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british_spl = mcsm_data['ligand_outcome'].value_counts()
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# compare series values since index will differ from spelling change
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check = american_spl.values == british_spl.values
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if check.all():
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print('PASS: spelling change successfull'
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, '\nNo. of predicted affinity changes:\n', british_spl
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, '\n===============================================================')
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else:
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print('FAIL: spelling change unsucessfull'
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, '\nExpected:\n', american_spl
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, '\nGot:\n', british_spl
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, '\n===============================================================')
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#%%===========================================================================
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# check dtype in cols: ensure correct dtypes for cols
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print('Checking dtypes in all columns:\n', mcsm_data.dtypes
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, '\n===================================================================')
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#1) numeric cols
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print('Converting the following cols to numeric:'
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, '\nligand_distance'
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, '\nduet_stability_change'
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, '\nligand_affinity_change'
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, '\n===================================================================')
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# using apply method to change stabilty and affinity values to numeric
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numeric_cols = ['duet_stability_change', 'ligand_affinity_change', 'ligand_distance']
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mcsm_data[numeric_cols] = mcsm_data[numeric_cols].apply(pd.to_numeric)
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# check dtype in cols
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print('checking dtype after conversion')
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cols_check = mcsm_data.select_dtypes(include='float64').columns.isin(numeric_cols)
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if cols_check.all():
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print('PASS: dtypes for selected cols:', numeric_cols
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, '\nchanged to numeric'
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, '\n===============================================================')
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else:
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print('FAIL:dtype change to numeric for selected cols unsuccessful'
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, '\n===============================================================')
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#mcsm_data['ligand_distance', 'ligand_affinity_change'].apply(is_numeric_dtype(mcsm_data['ligand_distance', 'ligand_affinity_change']))
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print(mcsm_data.dtypes)
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#%%===========================================================================
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# Normalise values in DUET_change col b/w -1 and 1 so negative numbers
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# stay neg and pos numbers stay positive
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duet_min = mcsm_data['duet_stability_change'].min()
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duet_max = mcsm_data['duet_stability_change'].max()
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duet_scale = lambda x : x/abs(duet_min) if x < 0 else (x/duet_max if x >= 0 else 'failed')
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mcsm_data['duet_scaled'] = mcsm_data['duet_stability_change'].apply(duet_scale)
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print('Raw duet scores:\n', mcsm_data['duet_stability_change']
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, '\n---------------------------------------------------------------'
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, '\nScaled duet scores:\n', mcsm_data['duet_scaled'])
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#%%===========================================================================
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# Normalise values in affinity change col b/w -1 and 1 so negative numbers
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# stay neg and pos numbers stay positive
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aff_min = mcsm_data['ligand_affinity_change'].min()
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aff_max = mcsm_data['ligand_affinity_change'].max()
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aff_scale = lambda x : x/abs(aff_min) if x < 0 else (x/aff_max if x >= 0 else 'failed')
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mcsm_data['ligand_affinity_change']
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mcsm_data['affinity_scaled'] = mcsm_data['ligand_affinity_change'].apply(aff_scale)
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mcsm_data['affinity_scaled']
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print('Raw affinity scores:\n', mcsm_data['ligand_affinity_change']
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, '\n---------------------------------------------------------------'
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, '\nScaled affinity scores:\n', mcsm_data['affinity_scaled'])
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#=============================================================================
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# Adding colname: wild_pos: sometimes useful for plotting and db
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print('Creating column: wild_pos')
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mcsm_data['wild_pos'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str)
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print(mcsm_data['wild_pos'].head())
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# Remove spaces b/w pasted columns
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print('removing white space within column: wild_position')
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mcsm_data['wild_pos'] = mcsm_data['wild_pos'].str.replace(' ', '')
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print('Correctly formatted column: wild_pos\n', mcsm_data['wild_pos'].head()
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, '\n===================================================================')
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#=============================================================================
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#%% Adding colname: wild_chain_pos: sometimes useful for plotting and db and is explicit
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print('Creating column: wild_chain_pos')
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mcsm_data['wild_chain_pos'] = mcsm_data['wild_type'] + mcsm_data['chain'] + mcsm_data['position'].astype(str)
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print(mcsm_data['wild_chain_pos'].head())
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# Remove spaces b/w pasted columns
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print('removing white space within column: wild_chain_pos')
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mcsm_data['wild_chain_pos'] = mcsm_data['wild_chain_pos'].str.replace(' ', '')
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print('Correctly formatted column: wild_chain_pos\n', mcsm_data['wild_chain_pos'].head()
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, '\n===================================================================')
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#=============================================================================
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#%% ensuring dtypes are string for the non-numeric cols
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#) char cols
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char_cols = ['PredAffLog', 'mutation_information', 'wild_type', 'mutant_type', 'chain'
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, 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_pos', 'wild_chain_pos']
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#mcsm_data[char_cols] = mcsm_data[char_cols].astype(str)
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cols_check_char = mcsm_data.select_dtypes(include='object').columns.isin(char_cols)
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if cols_check_char.all():
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print('PASS: dtypes for char cols:', char_cols, 'are indeed string'
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, '\n===============================================================')
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else:
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print('FAIL:dtype change to numeric for selected cols unsuccessful'
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, '\n===============================================================')
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#mcsm_data['ligand_distance', 'ligand_affinity_change'].apply(is_numeric_dtype(mcsm_data['ligand_distance', 'ligand_affinity_change']))
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print(mcsm_data.dtypes)
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#%%
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#=============================================================================
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#%% Removing PredAff log column as it is not needed?
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print('Removing col: PredAffLog since relevant info has been extracted from it')
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mcsm_data_f = mcsm_data.drop(columns = ['PredAffLog'])
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print(mcsm_data_f.head())
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#=============================================================================
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#%% sort df by position for convenience
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print('Sorting df by position')
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mcsm_data_fs = mcsm_data_f.sort_values(by = ['position'])
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print('sorted df:\n', mcsm_data_fs.head())
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#%%===========================================================================
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expected_ncols_toadd = 6 # beware of hardcoded numbers
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dforig_len = dforig_shape[1]
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expected_cols = dforig_len + expected_ncols_toadd
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if len(mcsm_data_fs.columns) == expected_cols:
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print('PASS: formatting successful'
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, '\nformatted df has expected no. of cols:', expected_cols
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, '\ncolnames:', mcsm_data_fs.columns
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, '\n----------------------------------------------------------------'
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, '\ndtypes in cols:', mcsm_data_fs.dtypes
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, '\n----------------------------------------------------------------'
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, '\norig data shape:', dforig_shape
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, '\nformatted df shape:', mcsm_data_fs.shape
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, '\n===============================================================')
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else:
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print('FAIL: something went wrong in formatting df'
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, '\nLen of orig df:', dforig_len
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, '\nExpected number of cols to add:', expected_ncols_toadd
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, '\nExpected no. of cols:', expected_cols, '(', dforig_len, '+', expected_ncols_toadd, ')'
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, '\nGot no. of cols:', len(mcsm_data_fs.columns)
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, '\nCheck formatting:'
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, '\ncheck hardcoded value:', expected_ncols_toadd
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, '\nis', expected_ncols_toadd, 'the no. of expected cols to add?'
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, '\n===============================================================')
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#%%============================================================================
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# writing file
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print('Writing formatted df to csv')
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mcsm_data_fs.to_csv(outfile_mcsm_norm, index = False)
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print('Finished writing file:'
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, '\nFile:', outfile_mcsm_norm
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, '\nExpected no. of rows:', len(mcsm_data_fs)
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, '\nExpected no. of cols:', len(mcsm_data_fs.columns)
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, '\n=============================================================')
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#%%
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#End of script
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