# -*- coding: utf-8 -*-
"""
Functions to read Percolator processed Kojak data.
"""
import numpy as np
import pandas as pd
if __name__ == '__main__':
import HelperFunctions as hf
import KojakFunctions as kj
else:
from . import HelperFunctions as hf
from . import KojakFunctions as kj
[docs]def Read(perc_files, rawfile=None, validated_string='.validated', percolator_string='.perc', decoy_string='decoy', compact=False, col_order=None):
"""
Collects unprocessed and percolated results and returns an xtable data array.
Args:
perc_file (str): path or list of paths to percolated Kojak file(s)
validated_string (str): user-defined string appended to the percolated filenames
percolator_string (str): user-defined string appended to the file prepared for percolating
decoy_string (optional): string used in kojak to label decoys
rawfile (str): name of the corresponding rawfile
col_order (list): List of xTable column titles that are used to sort and compress the resulting datatable
compact (bool): Whether to compact the xTable to only those columns listed in col_order
Returns:
pandas.DataFrame: xtable data table
"""
# convert to list if the input is only a single path
if not isinstance(perc_files, list):
perc_files = [perc_files]
allData = list()
kojak_dtypes = {'Scan Number': pd.Int64Dtype(),
'Charge': pd.Int64Dtype(),
'Link #1': pd.Int64Dtype(),
'Link #2': pd.Int64Dtype(),
'Score': float
}
for p_file in perc_files:
### Collect data and convert to pandas format
print('[Kojak Perc Read] Reading Percolator-file: ' + p_file)
try:
percolated = pd.read_csv(hf.compatible_path(p_file),
delimiter='\t',
usecols=range(5),
index_col=False, # avoid taking the first col as index
engine='python')
if len(percolated) == 0:
raise Exception("The file {} seems to be empty and cannot be converted".format(p_file))
except FileNotFoundError:
raise Exception("Could not find the percolated file %s." % p_file)
percolated.rename(columns={'PSMId': 'SpecId'}, inplace=True)
unperc_file = p_file.replace(validated_string, '')
print('[Kojak Perc Read] Reading Percolator input: ' + unperc_file)
try:
unpercolated = pd.read_csv(hf.compatible_path(unperc_file),
delimiter = '\t',
usecols=range(10),
engine='python',
index_col=False)
except FileNotFoundError:
raise Exception("Could not find the unpercolated file %s. Please move it into the same directory as the percolator files!" % unperc_file)
# Merge with left join (only keys that are in tje percolated DF will be re-
# tained)
xtable = pd.merge(percolated, unpercolated, on='SpecId', how='left')
xtable = xtable.rename(columns={'score': 'percolator_score'})
# Reading the Kojak-file is required to get additional information on the
# matches such as the corresponding protein names
kojak_file = unperc_file[0:unperc_file.find(percolator_string)] + '.kojak.txt'
print('Reading Kojak-file: ' + kojak_file)
try:
kojak = pd.read_csv(hf.compatible_path(kojak_file),
skiprows = 1, # skip the Kojak version
dtype=kojak_dtypes,
na_values='-',
delimiter='\t')
except FileNotFoundError:
raise Exception("Could not find the kojak_file %s. Please move it into the same directory as the percolator files!" % kojak_file)
kojak.rename(columns={'Scan Number': 'scannr'}, inplace=True)
s = pd.merge(xtable, kojak, on=['scannr', 'Charge', 'dScore', 'Score'], how='left')
allData.append(s)
xtable = pd.concat(allData, sort=False)
# split ambiguous concatenated protein names
xtable = hf.split_concatenated_lists(xtable, where=['Protein #1', 'Protein #2'])
print('[Kojak Perc Read] Splitted concatenated lists')
### Process the data to comply to xTable format
xtable = xtable.rename(columns={'scannr': 'scanno',
'Charge': 'prec_ch',
'Link #1': 'xlink1',
'Link #2': 'xlink2',
'Score': 'score'
})
print('[Kojak Perc Read] Renamed columns')
# Extract peptide sequence, modification mass and position from the
# Peptide #1 and Peptide #2 entries
xtable = kj.extract_peptide(xtable)
print('[Kojak Perc Read] Extracted peptides')
# transform unset xlinks to np.nan
xtable[['xlink1', 'xlink2']] = xtable[['xlink1', 'xlink2']].replace(-1, np.nan)
# extract protein name and relative cross-link position from the Protein #
# entries
xtable = kj.extract_protein(xtable)
print('[Kojak Perc Read] Extracted Proteins')
# calculate absolute position of first AA of peptide
# ignoring errors avoids raising error in case on NaN -> returns NaN
# as pos
# Must be calculated as float as NaN is not implemented in int
xtable['pos1'] =\
xtable['xpos1'].astype(float, errors='ignore') - \
xtable['xlink1'].astype(float, errors='ignore') + 1
xtable['pos2'] =\
xtable['xpos2'].astype(float, errors='ignore') - \
xtable['xlink2'].astype(float, errors='ignore') + 1
# Calculate if a cross link is of inter or of loop type
# Refine the inter type into inter/intra/homomultimeric
# Generate ID for the xlinks
xtable = kj.assign_ID_and_type(xtable)
print('[Kojak Perc Read] Calculated Positions and assigned IDs')
#sets the column decoy based on whether the decoy string is present in the
# protein name or not
xtable = kj.set_decoy(xtable, decoy_string)
# set the rawfile name for xtable (None if not provided by call)
xtable['rawfile'] = rawfile
xtable['xtype'] = np.nan
xtable['search_engine'] = 'Kojak and Percolator'
xtable = hf.order_columns(xtable, col_order, compact)
return xtable
if __name__ == '__main__':
# defines the column headers required for xtable output
col_order = [ 'rawfile', 'scanno', 'prec_ch',
'pepseq1', 'xlink1',
'pepseq2', 'xlink2', 'xtype',
'modmass1', 'modpos1', 'mod1',
'modmass2', 'modpos2', 'mod2',
'prot1', 'xpos1', 'prot2',
'xpos2', 'type', 'score', 'ID', 'pos1', 'pos2', 'decoy']
perc_file = [r'C:\Users\User\Documents\03_software\python\CroCo\testdata\PK\kojak_perc\20180615_KS_CL_9_msconvert.perc.intra.validated.txt',
#r'C:\Users\User\Documents\03_software\python\CroCo\testdata\PK\kojak_perc\20180615_KS_CL_9_msconvert.perc.loop.validated.txt',
r'C:\Users\User\Documents\03_software\python\CroCo\testdata\PK\kojak_perc\20180615_KS_CL_9_msconvert.perc.single.validated.txt']
xtable = Read(perc_file)
