added script to format results
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mcsm/format_results.py
Executable file
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mcsm/format_results.py
Executable file
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#!/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 = 'isoniazid'
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gene = 'KatG'
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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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# data dir
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#==========
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datadir = homedir + '/' + 'git/Data'
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#5am0chod
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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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outdir = datadir + '/' + drug + '/' + 'output'
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in_filename = gene.lower() + '_mcsm_output.csv' #(outfile, from mcsm_results)
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infile = outdir + '/' + in_filename
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print('Input filename:', in_filename
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, '\nInput path(from output dir):', outdir
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, '\n=============================================================')
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#=======
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# output
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#=======
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outdir = datadir + '/' + drug + '/' + 'output'
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#out_filename = gene.lower() + '.csv'
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#outfile = outdir + '/' + out_filename
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#print('Output filename:', out_filename
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# , '\nOutput path:', outdir
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# , '\n=============================================================')
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#=======================================================================
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mcsm_data = pd.read_csv(infile, 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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# change colnames to reflect units and no spaces, and replace '-' with '-'
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my_colnames_dict = {'Predicted Affinity Change': 'PredAffLog'
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, 'Mutation information': 'Mutationinformation'
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, 'Wild-type': 'Wild_type'
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, 'Position': 'Position'
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, 'Mutant-type': 'Mutant_type'
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, 'Chain': 'Chain'
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, 'Ligand ID': 'LigandID'
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, 'Distance to ligand': 'Dis_lig_Ang'
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, 'DUET stability change': 'DUET_change_kcalpermol'}
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mcsm_data.rename(columns = my_colnames_dict, inplace = True)
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mcsm_data.columns
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#%%===========================================================================
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# Extract only the numeric part from col: Dis_lig_Ang
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# number: '-?\d+\.?\d*'
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mcsm_data['Dis_lig_Ang']
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print('extracting numeric part of col: Dis_lig_Ang')
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mcsm_data['Dis_lig_Ang'] = mcsm_data['Dis_lig_Ang'].str.extract('(\d+\.?\d*)')
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mcsm_data['Dis_lig_Ang']
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# changing dtype to numeric
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#if is_numeric_dtype(mcsm_data['Dis_lig_Ang']):
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# print('Data type is already numeric, doing nothing')
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#else:
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# print('Changing dtype in col: Dis_lig_Ang to numeric since Distance should be numeric')
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## FIXME: either do it here, or in the end for all the required cols at once
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#%%===========================================================================
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# populate mutationinformation column:mcsm style muts {WT}<POS>{MUT}
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print('populating column : Mutationinformation which is currently empty\n', mcsm_data['Mutationinformation'])
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mcsm_data['Mutationinformation'] = mcsm_data['Wild_type'] + mcsm_data['Position'].astype(str) + mcsm_data['Mutant_type']
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print('checking after populating:\n', mcsm_data['Mutationinformation'])
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# Remove spaces b/w pasted columns
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print('removing white space within column: \Mutationinformation')
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mcsm_data['Mutationinformation'] = mcsm_data['Mutationinformation'].str.replace(' ', '')
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print('Correctly formatted column: Mutationinformation\n', mcsm_data['Mutationinformation'])
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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['Mutationinformation'].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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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(['Mutationinformation'])
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print('Dim of data after removing duplicate muts:', mcsm_data.shape)
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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_change_kcalpermol']>=0].count()
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DUET_pos = c.get(key = 'DUET_change_kcalpermol')
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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_change_kcalpermol']>=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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#%%===========================================================================
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# create Lig_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('Creating affinity change columns...')
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print('Extracting numerical and categorical parts from the col: PredAffLog')
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#aff_regex = re.compile(r'\b(\w+ing)\b')
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mcsm_data['affinity_change_log'] = mcsm_data['PredAffLog'].str.extract('(-?\d+\.?\d*)', expand = True)
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print(mcsm_data['affinity_change_log'])
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mcsm_data['Lig_outcome']= mcsm_data['PredAffLog'].str.extract(r'(\b\w+ing\b)', expand = True)
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print(mcsm_data['Lig_outcome'])
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print(mcsm_data['Lig_outcome'].value_counts())
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american_spl = mcsm_data['Lig_outcome'].value_counts()
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print('Changing to Bristish spellings for col: Lig_outcome')
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mcsm_data['Lig_outcome'].replace({'Destabilizing': 'Destabilising', 'Stabilizing': 'Stabilising'}, inplace = True)
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print(mcsm_data['Lig_outcome'].value_counts())
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british_spl = mcsm_data['Lig_outcome'].value_counts()
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# since series object will have different names on account of our spelling change
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# use .equals
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if american_spl.equals(british_spl):
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print('PASS: spelling change successfull')
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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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#%%===========================================================================
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# check dtype in cols
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print(mcsm_data.dtypes)
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# using apply method to change stabilty and affinity values to numeric
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mcsm_data[['affinity_change_log'
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, 'DUET_change_kcalpermol'
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, 'Dis_lig_Ang']] = mcsm_data[['affinity_change_log'
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, 'DUET_change_kcalpermol'
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, 'Dis_lig_Ang']].apply(pd.to_numeric)
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# check dtype in cols
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print(mcsm_data.dtypes)
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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_dataf = mcsm_data.drop(columns = ['PredAffLog'])
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#%%===========================================================================
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#%%===========================================================================
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# Normalise the DUET and affinity change cols:
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