134 lines
No EOL
5.7 KiB
Python
134 lines
No EOL
5.7 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Wed Aug 19 14:33:51 2020
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@author: tanu
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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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from bs4 import BeautifulSoup
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import pandas as pd
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import numpy as np
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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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#%%#####################################################################
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def format_mcsm_na_output(mcsm_na_output_tsv):
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"""
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@param mcsm_na_outputcsv: file containing mcsm_na_results for all muts
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which is the result of combining all mcsm_na batch results, and using
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bash scripts to combine all the batch results into one file.
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This is post run_get_results_mcsm_na.py
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Formatting df to a pandas df and output as csv.
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@type string
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@return (not true) formatted csv for mcsm_na 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_na_data_raw = pd.read_csv(mcsm_na_output_tsv, sep = '\t')
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# strip white space from both ends in all columns
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mcsm_na_data = mcsm_na_data_raw.apply(lambda x: x.str.strip() if x.dtype == 'object' else x)
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dforig_shape = mcsm_na_data.shape
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print('dimensions of input file:', dforig_shape)
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#############
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# rename cols
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#############
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# format colnames: all lowercase and consistent colnames
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mcsm_na_data.columns
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print('Assigning meaningful colnames'
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, '\n=======================================================')
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my_colnames_dict = {'PDB_FILE': 'pdb_file' # relevant info from this col will be extracted and the column discarded
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, 'CHAIN': 'chain'
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, 'WILD_RES': 'wild_type' # one letter amino acid code
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, 'RES_POS': 'position' # number
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, 'MUT_RES': 'mutant_type' # one letter amino acid code
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, 'RSA': 'rsa' # single letter (caps)
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, 'PRED_DDG': 'mcsm_na_affinity'} # 3-letter code
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mcsm_na_data.rename(columns = my_colnames_dict, inplace = True)
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mcsm_na_data.columns
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#%%============================================================================
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#############
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# create mutationinformation column
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#############
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#mcsm_na_data['mutationinformation'] = mcsm_na_data['wild_type'] + mcsm_na_data.position.map(str) + mcsm_na_data['mutant_type']
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mcsm_na_data['mutationinformation'] = mcsm_na_data.loc[:,'wild_type'] + mcsm_na_data.loc[:,'position'].astype(int).apply(str) + mcsm_na_data.loc[:,'mutant_type']
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#%%=====================================================================
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#############
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# Create col: mcsm_na_outcome
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#############
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# classification based on mcsm_na_affinity values
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print('Assigning col: mcsm_na_outcome based on mcsm_na_affinity')
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print('Sanity check:')
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# count positive values in the mcsm_na_affinity column
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c = mcsm_na_data[mcsm_na_data['mcsm_na_affinity']>=0].count()
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mcsm_na_pos = c.get(key = 'mcsm_na_affinity')
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# Assign category based on sign (+ve : I_affinity, -ve: R_affinity)
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mcsm_na_data['mcsm_na_outcome'] = np.where(mcsm_na_data['mcsm_na_affinity']>=0, 'Increased_affinity', 'Reduced_affinity')
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print('mcsm_na Outcome:', mcsm_na_data['mcsm_na_outcome'].value_counts())
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#if mcsm_na_pos == mcsm_na_data['mcsm_na_outcome'].value_counts()['Increased_affinity']:
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# print('PASS: mcsm_na_outcome assigned correctly')
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#else:
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# print('FAIL: mcsm_na_outcome assigned incorrectly'
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# , '\nExpected no. of Increased_affinity mutations:', mcsm_na_pos
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# , '\nGot no. of Increased affinity mutations', mcsm_na_data['mcsm_na_outcome'].value_counts()['Increased_affinity']
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# , '\n======================================================')
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#%%=====================================================================
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#############
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# scale mcsm_na values
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#############
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# Rescale values in mcsm_na_affinity col b/w -1 and 1 so negative numbers
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# stay neg and pos numbers stay positive
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mcsm_na_min = mcsm_na_data['mcsm_na_affinity'].min()
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mcsm_na_max = mcsm_na_data['mcsm_na_affinity'].max()
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mcsm_na_scale = lambda x : x/abs(mcsm_na_min) if x < 0 else (x/mcsm_na_max if x >= 0 else 'failed')
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mcsm_na_data['mcsm_na_scaled'] = mcsm_na_data['mcsm_na_affinity'].apply(mcsm_na_scale)
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print('Raw mcsm_na scores:\n', mcsm_na_data['mcsm_na_affinity']
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, '\n---------------------------------------------------------------'
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, '\nScaled mcsm_na scores:\n', mcsm_na_data['mcsm_na_scaled'])
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c2 = mcsm_na_data[mcsm_na_data['mcsm_na_scaled']>=0].count()
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mcsm_na_pos2 = c2.get(key = 'mcsm_na_affinity')
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if mcsm_na_pos == mcsm_na_pos2:
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print('\nPASS: Affinity values scaled correctly')
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else:
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print('\nFAIL: Affinity values scaled numbers MISmatch'
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, '\nExpected number:', mcsm_na_pos
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, '\nGot:', mcsm_na_pos2
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, '\n======================================================')
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#%%=====================================================================
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#############
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# reorder columns
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#############
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mcsm_na_data.columns
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mcsm_na_dataf = mcsm_na_data[['mutationinformation'
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, 'mcsm_na_affinity'
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, 'mcsm_na_scaled'
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, 'mcsm_na_outcome'
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, 'rsa'
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, 'wild_type'
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, 'position'
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, 'mutant_type'
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, 'chain'
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, 'pdb_file']]
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return(mcsm_na_dataf)
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#%%##################################################################### |