moved mmcsm_provean_ed_combine_CHECKS.py to scratch for when I need ED data for merging
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Wed May 25 02:35:41 2022
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@author: tanu
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"""
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import sys, os
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import pandas as pd
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from pandas import DataFrame
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import numpy as np
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import argparse
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from functools import reduce
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from sklearn.preprocessing import MinMaxScaler
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#%%
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mmcsm_lig_raw = pd.read_csv('/home/tanu/git/Data/pyrazinamide/output/mmcsm_lig/single_muts/pnca_mmcsm_results.csv'
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, index_col = False)
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mmcsm_lig_raw = pd.read_csv('/home/tanu/git/Data/cycloserine/output/mmcsm_lig/single_muts/alr_mmcsm_results.csv'
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, index_col = False)
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mmcsm_lig_raw = pd.read_csv('/home/tanu/git/Data/ethambutol/output/mmcsm_lig/single_muts/embb_mmcsm_results.csv'
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, index_col = False)
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mmcsm_lig_raw = pd.read_csv('/home/tanu/git/Data/streptomycin/output/mmcsm_lig/single_muts/gid_mmcsm_results.csv'
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, index_col = False)
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mmcsm_lig_raw = pd.read_csv('/home/tanu/git/Data/isoniazid/output/mmcsm_lig/single_muts/katg_mmcsm_results.csv'
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, index_col = False)
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mmcsm_lig_raw = pd.read_csv('/home/tanu/git/Data/rifampicin/output/mmcsm_lig/single_muts/rpob_mmcsm_results.csv'
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, index_col = False)
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mmcsm_lig_raw.columns
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# drop 1st column
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#mmcsm_lig = mmcsm_lig_raw.iloc[:,1:]
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# extract specific columns: might be simplers
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mmcsm_lig_df = mmcsm_lig_raw[['MUTATION', 'CHAIN', 'DDG']]
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mmcsm_lig_df['CHAIN'].value_counts()
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# Drop the chain column
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mmcsm_lig_df.drop(['CHAIN'], axis = 1, inplace = True)
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# Rename columns using lower case and consistently to allow merge later on
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mmcsm_lig_df.rename({'MUTATION': 'mutationinformation'
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, 'DDG': 'mmcsm_lig'}, axis = 1, inplace = True)
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#----------------------------------------
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# Rescale values in mmcsm_lig_affinity
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# col b/w -1 and 1 so negative numbers
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# stay neg and pos numbers stay positive
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#-----------------------------------------
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mmcsm_lig_min = mmcsm_lig_df['mmcsm_lig'].min()
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mmcsm_lig_max = mmcsm_lig_df['mmcsm_lig'].max()
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print('\nmmcsm_lig (MIN):', mmcsm_lig_min
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, '\nmmcsm_lig (MAX):', mmcsm_lig_max)
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# quick check
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print('\nNo. of Stabilising mmCSM mutations:', len(mmcsm_lig_df.loc[mmcsm_lig_df['mmcsm_lig'] >= 0]))
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print('\nNo. of Destabilising mmCSM mutations:', len(mmcsm_lig_df.loc[mmcsm_lig_df['mmcsm_lig'] < 0]))
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mmcsm_ligscale = lambda x : x/abs(mmcsm_lig_min) if x < 0 else (x/mmcsm_lig_max if x >= 0 else 'failed')
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mmcsm_lig_df['mmcsm_lig_scaled'] = mmcsm_lig_df.loc[:,'mmcsm_lig'].apply(mmcsm_ligscale)
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print('\nRaw mmcsm_lig scores:\n', mmcsm_lig_df['mmcsm_lig']
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, '\n---------------------------------------------------------------'
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, '\nScaled mmcsm_lig scores:\n', mmcsm_lig_df['mmcsm_lig_scaled'])
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print('\nmmCSM lig raw (Max):', mmcsm_lig_df['mmcsm_lig'].max()
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, '\nmmCSM lig scaled (Max):', mmcsm_lig_df['mmcsm_lig_scaled'].max())
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print('\nmmCSM lig raw (Min):', mmcsm_lig_df['mmcsm_lig'].min()
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, '\nmmCSM lig scaled (Min):', mmcsm_lig_df['mmcsm_lig_scaled'].min())
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mmcsm_lig_df['mmcsm_lig_scaled'].hist(bins = 30)
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mmcsm_lig_df['mmcsm_lig'].hist(bins = 30)
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#-----------------------------
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# mmCSM lig outcome category:
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# -ve: Destabilising
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# +ve: Stabilising
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#----------------------------
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mmcsm_lig_df['mmcsm_lig_outcome'] = mmcsm_lig_df.loc[:,'mmcsm_lig'].apply(lambda x: 'Destabilising' if x < 0 else 'Stabilising')
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mmcsm_lig_df[mmcsm_lig_df['mmcsm_lig']<0].count()
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###############################################################################
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#%% Provean data
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provean_df = pd.read_csv('/home/tanu/git/Data/pyrazinamide/output/provean/pnca_provean.csv'
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, header = None)
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provean_df = pd.read_csv('/home/tanu/git/Data/cycloserine/output/provean/alr_provean.csv'
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, header = None)
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provean_df = pd.read_csv('/home/tanu/git/Data/ethambutol/output/provean/embb_provean.csv'
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, header = None)
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provean_df = pd.read_csv('/home/tanu/git/Data/streptomycin/output/provean/gid_provean.csv'
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, header = None)
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provean_df = pd.read_csv('/home/tanu/git/Data/isoniazid/output/provean/katg_provean.csv'
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, header = None)
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provean_df = pd.read_csv('/home/tanu/git/Data/rifampicin/output/provean/rpob_provean.csv'
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, header = None)
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provean_df.head()
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provean_df.columns = ['mutationinformation', 'provean_score', 'provean_outcome']
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provean_df.head()
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provean_df['provean_outcome'].value_counts()
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#----------------------------------------
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# Rescale values in provean_score
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# col b/w -1 and 1 so negative numbers
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# stay neg and pos numbers stay positive
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# cut off =-2.5
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# so provean scores >= (-2.5) are neutral
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# and provean scores < (2.5) are deleterious
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#-----------------------------------------
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provean_min = provean_df['provean_score'].min()
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provean_max = provean_df['provean_score'].max()
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print('\nprovean_score (MIN):', provean_min
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, '\nprovean_score (MAX):', provean_max)
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# quick check
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provean_cut_off = -2.5
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if (provean_df['provean_score'] > provean_cut_off).sum() == provean_df['provean_outcome'].value_counts()['Neutral']:
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print('\nPASS: Provean cut off is indeed:', provean_cut_off
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, '\nNo. of values above', provean_cut_off, 'i.e classed as Neutral:'
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, (provean_df['provean_score']>provean_cut_off).sum()
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, '\nProvean outcome:'
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, '\nNeutral:', len(provean_df.loc[provean_df['provean_score'] > provean_cut_off])
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, '\nDeleterious:', len(provean_df.loc[provean_df['provean_score'] < provean_cut_off])
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)
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else:
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sys.exit('\nFAIL: Numbers mismatch. Please check provean cut off and condition used')
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# RECHECK logic!: CANNOT use this as it changes the data distribution, as seen from the his plot
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# provean_scale = lambda x : x/abs(provean_min) if x < 0 else (x/provean_min if x >= 0 else 'failed')
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# provean_df['provean_scaled1'] = provean_df.loc[:,'provean_score'].apply(provean_scale)
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# print('\nRaw provean scores:\n', provean_df['provean_score']
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# , '\n---------------------------------------------------------------'
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# , '\nScaled provean scores:\n', provean_df['provean_scaled'])
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# print('\nprovean raw (Max):' , provean_df['provean_score'].max()
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# , '\nprovean scaled (Max):' , provean_df['provean_scaled1'].max())
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# print('\nprovean raw (Min):' , provean_df['provean_score'].min()
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# , '\nprovean scaled (Min):' , provean_df['provean_scaled1'].min())
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scaler = MinMaxScaler()
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provean_df['provean_scaled'] = scaler.fit_transform(provean_df['provean_score'].values.reshape(-1,1))
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provean_df['provean_score'].hist(bins = 30)
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#provean_df['provean_scaled1'].hist(bins = 10)
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provean_df['provean_scaled'].hist(bins = 30)
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