bring in embb stuff which was in the wrong branch
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6 changed files with 816 additions and 98 deletions
159
mcsm_ppi2/format_results_mcsm_ppi2.py
Executable file
159
mcsm_ppi2/format_results_mcsm_ppi2.py
Executable file
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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 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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homedir = os.path.expanduser('~')
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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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sys.path.append(homedir + '/git/LSHTM_analysis/scripts')
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from reference_dict import up_3letter_aa_dict
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from reference_dict import oneletter_aa_dict
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#%%#####################################################################
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def format_mcsm_ppi2_output(mcsm_ppi2_output_csv):
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"""
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@param mcsm_ppi2_output_csv: file containing mcsm_ppi2_results for all muts
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which is the result of combining all mcsm_ppi2 batch results, and using
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bash scripts to combine all the batch results into one file.
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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_ppi2 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_ppi2_data_raw = pd.read_csv(mcsm_ppi2_output_csv, sep = ',')
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# strip white space from both ends in all columns
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mcsm_ppi2_data = mcsm_ppi2_data_raw.apply(lambda x: x.str.strip() if x.dtype == 'object' else x)
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dforig_shape = mcsm_ppi2_data.shape
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print('dimensions of input file:', dforig_shape)
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#############
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# Map 3 letter
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# code to one
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#############
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# initialise a sub dict that is lookup dict for
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# 3-LETTER aa code to 1-LETTER aa code
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lookup_dict = dict()
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for k, v in up_3letter_aa_dict.items():
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lookup_dict[k] = v['one_letter_code']
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wt = mcsm_ppi2_data['wild-type'].squeeze() # converts to a series that map works on
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mcsm_ppi2_data['w_type'] = wt.map(lookup_dict)
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mut = mcsm_ppi2_data['mutant'].squeeze()
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mcsm_ppi2_data['m_type'] = mut.map(lookup_dict)
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# #############
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# # CHECK
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# # Map 1 letter
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# # code to 3Upper
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# #############
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# # initialise a sub dict that is lookup dict for
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# # 3-LETTER aa code to 1-LETTER aa code
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# lookup_dict = dict()
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# for k, v in oneletter_aa_dict.items():
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# lookup_dict[k] = v['three_letter_code_upper']
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# wt = mcsm_ppi2_data['w_type'].squeeze() #converts to a series that map works on
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# mcsm_ppi2_data['WILD'] = wt.map(lookup_dict)
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# mut = mcsm_ppi2_data['m_type'].squeeze()
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# mcsm_ppi2_data['MUT'] = mut.map(lookup_dict)
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# # check
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# mcsm_ppi2_data['wild-type'].equals(mcsm_ppi2_data['WILD'])
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# mcsm_ppi2_data['mutant'].equals(mcsm_ppi2_data['MUT'])
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#%%============================================================================
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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_ppi2_data.columns
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print('Assigning meaningful colnames'
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, '\n=======================================================')
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my_colnames_dict = {'chain': 'chain'
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, 'wild-type': 'wt_upper'
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, 'res-number': 'position'
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, 'mutant': 'mut_upper'
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, 'distance-to-interface': 'interface_dist'
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, 'mcsm-ppi2-prediction': 'mcsm_ppi2_affinity'
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, 'affinity': 'mcsm_ppi2_outcome'
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, 'w_type': 'wild_type' # one letter amino acid code
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, 'm_type': 'mutant_type' # one letter amino acid code
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}
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mcsm_ppi2_data.rename(columns = my_colnames_dict, inplace = True)
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mcsm_ppi2_data.columns
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#############
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# create mutationinformation column
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#############
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#mcsm_ppi2_data['mutationinformation'] = mcsm_ppi2_data['wild_type'] + mcsm_ppi2_data.position.map(str) + mcsm_ppi2_data['mutant_type']
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mcsm_ppi2_data['mutationinformation'] = mcsm_ppi2_data.loc[:,'wild_type'] + mcsm_ppi2_data.loc[:,'position'].astype(int).apply(str) + mcsm_ppi2_data.loc[:,'mutant_type']
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#%%=====================================================================
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#########################
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# scale mcsm_ppi2 values
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#########################
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# Rescale values in mcsm_ppi2_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_ppi2_min = mcsm_ppi2_data['mcsm_ppi2_affinity'].min()
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mcsm_ppi2_max = mcsm_ppi2_data['mcsm_ppi2_affinity'].max()
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mcsm_ppi2_scale = lambda x : x/abs(mcsm_ppi2_min) if x < 0 else (x/mcsm_ppi2_max if x >= 0 else 'failed')
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mcsm_ppi2_data['mcsm_ppi2_scaled'] = mcsm_ppi2_data['mcsm_ppi2_affinity'].apply(mcsm_ppi2_scale)
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print('Raw mcsm_ppi2 scores:\n', mcsm_ppi2_data['mcsm_ppi2_affinity']
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, '\n---------------------------------------------------------------'
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, '\nScaled mcsm_ppi2 scores:\n', mcsm_ppi2_data['mcsm_ppi2_scaled'])
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c = mcsm_ppi2_data[mcsm_ppi2_data['mcsm_ppi2_affinity']>=0].count()
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mcsm_ppi2_pos = c.get(key = 'mcsm_ppi2_affinity')
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c2 = mcsm_ppi2_data[mcsm_ppi2_data['mcsm_ppi2_scaled']>=0].count()
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mcsm_ppi2_pos2 = c2.get(key = 'mcsm_ppi2_scaled')
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if mcsm_ppi2_pos == mcsm_ppi2_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_ppi2_pos
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, '\nGot:', mcsm_ppi2_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_ppi2_data.columns
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mcsm_ppi2_dataf = mcsm_ppi2_data[['mutationinformation'
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, 'mcsm_ppi2_affinity'
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, 'mcsm_ppi2_scaled'
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, 'mcsm_ppi2_outcome'
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, 'interface_dist'
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, 'wild_type'
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, 'position'
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, 'mutant_type'
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, 'wt_upper'
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, 'mut_upper'
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, 'chain']]
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return(mcsm_ppi2_dataf)
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#%%#####################################################################
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79
mcsm_ppi2/run_format_results_mcsm_ppi2.py
Executable file
79
mcsm_ppi2/run_format_results_mcsm_ppi2.py
Executable file
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@ -0,0 +1,79 @@
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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 Fri Feb 12 12:15:26 2021
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@author: tanu
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"""
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#%% load packages
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import sys, os
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homedir = os.path.expanduser('~')
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#sys.path.append(homedir + '/git/LSHTM_analysis/mcsm_ppi2')
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from format_results_mcsm_ppi2 import *
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########################################################################
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# TODO: add cmd line args
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#%% command line args
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arg_parser = argparse.ArgumentParser()
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arg_parser.add_argument('-d', '--drug' , help = 'drug name (case sensitive)', default = None)
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arg_parser.add_argument('-g', '--gene' , help = 'gene name (case sensitive)', default = None)
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arg_parser.add_argument('--datadir' , help = 'Data Directory. By default, it assmumes homedir + git/Data')
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arg_parser.add_argument('-i', '--input_dir' , help = 'Input dir containing pdb files. By default, it assmumes homedir + <drug> + input')
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arg_parser.add_argument('-o', '--output_dir', help = 'Output dir for results. By default, it assmes homedir + <drug> + output')
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#arg_parser.add_argument('--mkdir_name' , help = 'Output dir for processed results. This will be created if it does not exist')
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arg_parser.add_argument('-m', '--make_dirs' , help = 'Make dir for input and output', action='store_true')
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arg_parser.add_argument('--debug' , action = 'store_true' , help = 'Debug Mode')
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args = arg_parser.parse_args()
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#%%============================================================================
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# variable assignment: input and output paths & filenames
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drug = args.drug
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gene = args.gene
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datadir = args.datadir
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indir = args.input_dir
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outdir = args.output_dir
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#outdir_ppi2 = args.mkdir_name
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make_dirs = args.make_dirs
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#=======
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# dirs
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#=======
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if not datadir:
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datadir = homedir + '/git/Data/'
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if not indir:
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indir = datadir + drug + '/input/'
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if not outdir:
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outdir = datadir + drug + '/output/'
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#if not mkdir_name:
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# outdir_ppi2 = outdir + 'mcsm_ppi2/'
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outdir_ppi2 = outdir + 'mcsm_ppi2/'
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# Input file
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infile_mcsm_ppi2 = outdir_ppi2 + gene.lower() + '_output_combined_clean.csv'
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# Formatted output file
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outfile_mcsm_ppi2_f = outdir_ppi2 + gene.lower() + '_complex_mcsm_ppi2_norm.csv'
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#==========================
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# CALL: format_results_mcsm_na()
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# Data: gid+streptomycin
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#==========================
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print('Formatting results for:', infile_mcsm_ppi2)
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mcsm_ppi2_df_f = format_mcsm_ppi2_output(mcsm_ppi2_output_csv = infile_mcsm_ppi2)
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# writing file
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print('Writing formatted df to csv')
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mcsm_ppi2_df_f.to_csv(outfile_mcsm_ppi2_f, index = False)
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print('Finished writing file:'
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, '\nFile:', outfile_mcsm_ppi2_f
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, '\nExpected no. of rows:', len(mcsm_ppi2_df_f)
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, '\nExpected no. of cols:', len(mcsm_ppi2_df_f.columns)
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, '\n=============================================================')
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#%%#####################################################################
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