defined method for formatting mcsm_results
This commit is contained in:
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2 changed files with 245 additions and 206 deletions
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@ -6,9 +6,9 @@
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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 requests
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import re
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import time
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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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@ -43,7 +43,7 @@ datadir = homedir + '/' + 'git/Data'
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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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in_filename = gene.lower() + '_mcsm_output.csv' #(outfile, from mcsm_results.py)
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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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@ -53,215 +53,256 @@ print('Input filename:', in_filename
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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() + '_complex_mcsm_norm.csv'
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out_filename = gene.lower() + '_complex_mcsm_results.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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print('Reading input file')
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mcsm_data = pd.read_csv(infile, sep = ',')
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def format_mcsm_output(mcsm_outputcsv):
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"""
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@param mcsm_outputcsv: file containing mcsm results for all muts
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(outfile from from mcsm_results.py)
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@type string
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@return formatted mcsm output
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@type pandas df
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"""
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#############
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# Read file
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#############
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mcsm_data = pd.read_csv(infile, sep = ',')
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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.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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mcsm_data.rename(columns = my_colnames_dict, inplace = True)
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#%%===========================================================================
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#################################
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# populate mutation_information
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# col which is currently blank
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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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# change colnames to reflect units and no spaces, and replace '-' with '-'
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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'
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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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# 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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#%%===========================================================================
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#############
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# sanity check: drop dupliate muts
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#############
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# shouldn't exist as this should be eliminated at the time of running mcsm
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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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#############
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# Create col: duet_outcome
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#############
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# 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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#############
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# Extract numeric
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# part of ligand_distance col
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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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#############
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# Create 2 columns:
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# ligand_affinity_change and ligand_outcome
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#############
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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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# numerical part: '-?\d+\.?\d*'
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# categorocal part: '\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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mcsm_data.rename(columns = my_colnames_dict, inplace = True)
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mcsm_data.columns
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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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, '\n===================================================================')
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# 1) 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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# 2) 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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# 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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, '\n===================================================================')
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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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, '\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(['Mutationinformation'])
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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_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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, '\n===============================================================')
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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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#############
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# changing spelling: British
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#############
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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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#############
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# ensuring corrrect dtype columns
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#############
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# check dtype in cols
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print('Checking dtypes in all columns:\n', mcsm_data.dtypes
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, '\n===================================================================')
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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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print(mcsm_data.dtypes)
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#%%===========================================================================
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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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# 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('Extracting numerical and categorical parts from the col: PredAffLog')
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print('to create two columns: affinity_change_log and Lig_outcome'
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, '\n===================================================================')
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#############
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# scale duet values
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#############
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# Rescale 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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# Extracting the predicted affinity change (numerical part)
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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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# 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['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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duet_scale = lambda x : x/abs(duet_min) if x < 0 else (x/duet_max if x >= 0 else 'failed')
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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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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
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print('Checking dtypes in all columns:\n', mcsm_data.dtypes
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, '\n===================================================================')
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print('Converting the following cols to numeric:'
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, '\nDis_lig_Ang'
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, '\nDUET_change_kcalpermol'
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, '\naffinity_change_log'
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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_change_kcalpermol', 'affinity_change_log', 'Dis_lig_Ang']
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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['Dis_lig_Ang', 'affinity_change_log'].apply(is_numeric_dtype(mcsm_data['Dis_lig_Ang', 'affinity_change_log']))
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print(mcsm_data.dtypes)
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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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#############
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# scale affinity values
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#############
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# rescale 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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#%%===========================================================================
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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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#%%===========================================================================
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# Normalise the DUET and affinity change cols
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#converter = lambda x : x*2 if x < 10 else (x*3 if x < 20 else x)
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duet_min = mcsm_data['DUET_change_kcalpermol'].min()
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duet_max = mcsm_data['DUET_change_kcalpermol'].max()
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converter = 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_change_kcalpermol']
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mcsm_data['ratioDUET'] = mcsm_data['DUET_change_kcalpermol'].apply(converter)
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mcsm_data['ratioDUET']
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#%%===========================================================================
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||||
# Normalise the affinity change cols
|
||||
aff_min = mcsm_data['affinity_change_log'].min()
|
||||
aff_max = mcsm_data['affinity_change_log'].max()
|
||||
|
||||
converter = lambda x : x/abs(aff_min) if x < 0 else (x/aff_max if x >= 0 else 'failed')
|
||||
#converter(mcsm_data['affinity_change_log'])
|
||||
|
||||
mcsm_data['affinity_change_log']
|
||||
mcsm_data['ratioPredAff'] = mcsm_data['affinity_change_log'].apply(converter)
|
||||
mcsm_data['ratioPredAff']
|
||||
#=============================================================================
|
||||
# Removing PredAff log column as it is not needed?
|
||||
print('Removing col: PredAffLog since relevant info has been extracted from it')
|
||||
mcsm_dataf = mcsm_data.drop(columns = ['PredAffLog'])
|
||||
#%%===========================================================================
|
||||
expected_cols_toadd = 4
|
||||
dforig_len = dforig_shape[1]
|
||||
expected_cols = dforig_len + expected_cols_toadd
|
||||
if len(mcsm_dataf.columns) == expected_cols:
|
||||
print('PASS: formatting successful'
|
||||
, '\nformatted df has expected no. of cols:', expected_cols
|
||||
, '\n---------------------------------------------------------------'
|
||||
, '\ncolnames:', mcsm_dataf.columns
|
||||
, '\n----------------------------------------------------------------'
|
||||
, '\ndtypes in cols:', mcsm_dataf.dtypes
|
||||
, '\n----------------------------------------------------------------'
|
||||
, '\norig data shape:', dforig_shape
|
||||
, '\nformatted df shape:', mcsm_dataf.shape
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL: something went wrong in formatting df'
|
||||
, '\nExpected no. of cols:', expected_cols
|
||||
, '\nGot no. of cols:', len(mcsm_dataf.columns)
|
||||
, '\nCheck formatting'
|
||||
, '\n===============================================================')
|
||||
mcsm_data['affinity_scaled'] = mcsm_data['ligand_affinity_change'].apply(aff_scale)
|
||||
print('Raw affinity scores:\n', mcsm_data['ligand_affinity_change']
|
||||
, '\n---------------------------------------------------------------'
|
||||
, '\nScaled affinity scores:\n', mcsm_data['affinity_scaled'])
|
||||
#=============================================================================
|
||||
# Removing PredAff log column as it is not needed?
|
||||
print('Removing col: PredAffLog since relevant info has been extracted from it')
|
||||
mcsm_dataf = mcsm_data.drop(columns = ['PredAffLog'])
|
||||
#%%===========================================================================
|
||||
#############
|
||||
# sanity check before writing file
|
||||
#############
|
||||
expected_cols_toadd = 4
|
||||
dforig_len = dforig_shape[1]
|
||||
expected_cols = dforig_len + expected_cols_toadd
|
||||
if len(mcsm_dataf.columns) == expected_cols:
|
||||
print('PASS: formatting successful'
|
||||
, '\nformatted df has expected no. of cols:', expected_cols
|
||||
, '\n---------------------------------------------------------------'
|
||||
, '\ncolnames:', mcsm_dataf.columns
|
||||
, '\n----------------------------------------------------------------'
|
||||
, '\ndtypes in cols:', mcsm_dataf.dtypes
|
||||
, '\n----------------------------------------------------------------'
|
||||
, '\norig data shape:', dforig_shape
|
||||
, '\nformatted df shape:', mcsm_dataf.shape
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL: something went wrong in formatting df'
|
||||
, '\nExpected no. of cols:', expected_cols
|
||||
, '\nGot no. of cols:', len(mcsm_dataf.columns)
|
||||
, '\nCheck formatting'
|
||||
, '\n===============================================================')
|
||||
return mcsm_dataf
|
||||
#%%============================================================================
|
||||
# call function
|
||||
mcsm_df_formatted = format_mcsm_output(infile)
|
||||
|
||||
# writing file
|
||||
print('Writing formatted df to csv')
|
||||
mcsm_dataf.to_csv(outfile, index = False)
|
||||
mcsm_df_formatted.to_csv(outfile, index = False)
|
||||
|
||||
print('Finished writing file:'
|
||||
, '\nFilename:', out_filename
|
||||
, '\nPath:', outdir
|
||||
, '\nExpected no. of rows:', len(mcsm_dataf)
|
||||
, '\nExpected no. of cols:', len(mcsm_dataf.columns)
|
||||
, '\nExpected no. of rows:', len(mcsm_df_formatted)
|
||||
, '\nExpected no. of cols:', len(mcsm_df_formatted)
|
||||
, '\n=============================================================')
|
||||
#%%
|
||||
#End of script
|
||||
|
|
|
|||
|
|
@ -58,13 +58,10 @@ print('Output filename:', out_filename
|
|||
, '\nOutput path:', outdir
|
||||
, '\n=============================================================')
|
||||
|
||||
#%% global variables
|
||||
#HOST = "http://biosig.unimelb.edu.au"
|
||||
#PREDICTION_URL = f"{HOST}/mcsm_lig/prediction"
|
||||
#=======================================================================
|
||||
def fetch_results(urltextfile):
|
||||
"""
|
||||
Extract results data from the results page
|
||||
Extract results data using the prediction url
|
||||
|
||||
@params result_page of request_results()
|
||||
@type response object
|
||||
|
|
@ -90,36 +87,37 @@ def fetch_results(urltextfile):
|
|||
|
||||
def build_result_dict(web_result_raw):
|
||||
"""
|
||||
Format web results which is inconveniently preformatted!
|
||||
Build dict of mcsm output for a single mutation
|
||||
Format web results which is preformatted to enable building result dict
|
||||
# preformatted string object: Problematic!
|
||||
|
||||
# roughly bring these to the same format as the other
|
||||
# make format consistent
|
||||
|
||||
@params web_result_raw directly from html parser extraction
|
||||
@params web_result_raw: directly from html parser extraction
|
||||
@type string
|
||||
|
||||
@returns result dict
|
||||
@type {}
|
||||
"""
|
||||
|
||||
# remove blank lines from output
|
||||
|
||||
# remove blank lines from output
|
||||
mytext = os.linesep.join([s for s in web_result_raw.splitlines() if s])
|
||||
|
||||
# Predicted affintiy change and DUET stability change cols
|
||||
# are are split over multiple lines and Mutation information is empty!
|
||||
# affinity change and DUET stability change cols are are split over
|
||||
# multiple lines and Mutation information is empty!
|
||||
mytext = mytext.replace('ange:\n', 'ange: ')
|
||||
#print(mytext)
|
||||
# print(mytext)
|
||||
|
||||
# initiliase result_dict
|
||||
# initiliase result_dict
|
||||
result_dict = {}
|
||||
for line in mytext.split('\n'):
|
||||
fields = line.split(':')
|
||||
#print(fields)
|
||||
# print(fields)
|
||||
if len(fields) > 1: # since Mutaton information is empty
|
||||
dict_entry = dict([(x, y) for x, y in zip(fields[::2], fields[1::2])])
|
||||
result_dict.update(dict_entry)
|
||||
|
||||
return result_dict
|
||||
return result_dict
|
||||
|
||||
#=======================================================================
|
||||
#%% call function
|
||||
#request_results(infile_url)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue