# -*- coding: utf-8 -*-
"""
Functions to read xQuest data.
"""
import numpy as np
import pandas as pd
import re
if __name__ == '__main__':
import HelperFunctions as hf
else:
from . import HelperFunctions as hf
def _process_xquest_spectrum(spec_string):
"""
Extract rawfile name, precursor charge and scan no from xQuest spectrum
string
Args:
spec_string: xQuest spectrum string
Returns:
str or np.nan: rawfile name
int or np.nan: scan number
int or np.nan: precursor charge
"""
spectrum_pattern = re.compile('(.+)\.(\d+)\.\d+\..+\.\d+\.\d+\.(\d+)')
if spectrum_pattern.match(spec_string):
match = spectrum_pattern.match(spec_string)
rawfile, scanno, prec_ch = match.groups()
return str(rawfile), int(scanno), int(prec_ch)
else:
return np.nan
def _process_xquest_id(Id_string):
"""
Extract peptide sequence of the alpha (longer) and the beta (shorter)
peptide as well as the relative positions of the cross-links within
these sequences from an xQuest Id-string
Args:
ID_string (str): an xQuest Id-String
type (str): the xlink type from xQuest (monolink, inrtalink, xlink)
Returns:
str or np.nan: pepseq1
str or np.nan: pepseq2
int or np.nan: xlink1
int or np.nan: xlink2
"""
xlink_pattern = re.compile('^(\w+)-(\w+)-a(\d+)-b(\d+)')
intralink_pattern = re.compile('^(\w+)-\D{1}(\d+)-\D{1}(\d+)')
monolink_pattern = re.compile('^(\w+)-\D{1}(\d+)-\d+')
if xlink_pattern.match(Id_string):
match = xlink_pattern.match(Id_string)
# pepseq1, pepseq2, xlink1, xlink2
pepseq1, pepseq2, xlink1, xlink2 = match.groups()
# return pepseq1, pepseq2, xlink1, xlink2
return pepseq1, pepseq2, int(xlink1), int(xlink2)
elif intralink_pattern.match(Id_string):
match = intralink_pattern.match(Id_string)
# pepseq1, pepseq2, xlink1, xlink2
pepseq, xlink1, xlink2 = match.groups()
# return pepseq, pepseq, xlink1, xlink2
return pepseq, pepseq, int(xlink1), int(xlink2)
elif monolink_pattern.match(Id_string):
match = monolink_pattern.match(Id_string)
# pepseq1, pepseq2, xlink1, xlink2
pepseq, xlink = match.groups()
# return pepseq, np.nan, xlink, np.nan
return pepseq, np.nan, int(xlink), np.nan
else:
return np.nan, np.nan, np.nan, np.nan
def _categorize_xquest_type(XQType):
"""
Extract protein name and absolute cross-link position from
xQuest type string (xlink, loop, mono)
Args:
XQType (str): xquest type string
Returns:
str or np.nan: type of cross-link (inter, loop, mono)
"""
if XQType == 'xlink':
return 'inter'
elif XQType == 'intralink':
return 'loop'
elif XQType == 'monolink':
return 'mono'
else:
return np.nan
[docs]def Read(xQuest_files, col_order=None, compact=False):
"""
Read xQuest results file and return file in xTable format.
Args:
xQuest_files (list): path to xQuest results file(s)
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(xQuest_files, list):
xQuest_files = [xQuest_files]
allData = list()
xQuest_dtypes = {'z': pd.Int64Dtype(),
'Protein1': str,
'Protein2': str,
'AbsPos1': pd.Int64Dtype(),
'AbsPos2': pd.Int64Dtype(),
'ld-Score': float}
for file in xQuest_files:
### Collect data and convert to pandas format
print('Reading xQuest-file: ' + file)
# only called if inter_file is not None
# try:
s = pd.read_csv(hf.compatible_path(file),
delimiter='\t',
na_values='-',
dtype=xQuest_dtypes)
allData.append(s)
# except:
# raise Exception('[xQuest Read] Failed opening file: {}'.format(file))
xtable = pd.concat(allData)
rename_dict = {'z':'prec_ch',
'Protein1':'prot1',
'Protein2': 'prot2',
'AbsPos1': 'xpos1',
'AbsPos2': 'xpos2',
'ld-Score': 'score'}
# Copy and rename selected columns to new xquest df
try:
xtable.rename(index=str,
columns=rename_dict,
inplace=True)
except Exception as e:
raise Exception('[xQuest Read] Error during xQuest header renaming: %s' % e)
# Extract rawfile, scanno and precursor charge from the mgf header string
# used as Spectrum by xQuest
xtable[['rawfile', 'scanno', 'prec_ch']] =\
pd.DataFrame(xtable['Spectrum'].apply(_process_xquest_spectrum).tolist(), index=xtable.index)
print('[xQuest Read] Processed Spectrum entry')
# Extract peptide sequences and relative cross-link positions form the
# xQuest ID-string
xtable[['pepseq1', 'pepseq2', 'xlink1', 'xlink2']] =\
pd.DataFrame(xtable['Id'].apply(_process_xquest_id).tolist(), index=xtable.index)
print('[xQuest Read] Processed xQuest ID' )
# Modifications are not defined in xQuest
xtable['mod1'], xtable['mod2'] = "", ""
# calculate the absolute position of the first amino acide of the resp
# peptides
xtable['pos1'] = xtable['xpos1'] - xtable['xlink1'] + 1
xtable['pos2'] = xtable['xpos2'] - xtable['xlink2'] + 1
print('[xQuest Read] Calculated positions')
# Assign mono
xtable['type'] = xtable['Type'].apply(_categorize_xquest_type)
if len(xtable[xtable['type'] == 'inter']) > 0:
# Reassign the type for intra and inter xlink to inter/intra/homomultimeric
intraAndInter = (xtable['type'] == 'inter') | (xtable['type'] == 'intra')
xtable.loc[intraAndInter, 'type'] =\
np.vectorize(hf.categorize_inter_peptides)(xtable[intraAndInter]['prot1'],
xtable[intraAndInter]['pos1'],
xtable[intraAndInter]['pepseq1'],
xtable[intraAndInter]['prot2'],
xtable[intraAndInter]['pos2'],
xtable[intraAndInter]['pepseq2'])
print('[xQuest Read] categorized inter peptides')
else:
print('[xQuest Read] skipped inter peptide categorization')
# generate an ID for every crosslink position within the protein(s)
xtable['ID'] =\
pd.Series(np.vectorize(hf.generate_id,
otypes=['object'])(xtable['type'],
xtable['prot1'],
xtable['xpos1'],
xtable['prot2'],
xtable['xpos2']),
index=xtable.index).replace('nan', np.nan)
print('[xQuest Read] Generated ID')
# xQuest does not incorporate decoy entries in the results table
# but protein names can contain identifiers as reverse or decoy
xtable['decoy'] = xtable['ID'].str.contains('reverse') |\
xtable['ID'].str.contains('decoy')
# the following properties cannot directly be inferred from the
# xQuest results file
# to avoid confusion with missing valued like np.nan, they are set to
# UNKNOWN
for header in ['xtype', 'modmass1', 'modpos1', 'modmass2', 'modpos2']:
xtable[header] = np.nan
xtable['search_engine'] = 'xQuest'
xtable = hf.order_columns(xtable, col_order, compact)
### Return df
return xtable
if __name__ == '__main__':
"""
For testing purposes only
"""
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']
xtable = Read(r'C:\Users\User\Documents\03_software\python\CroCo\testdata\PK\xQuest\20190227_croco_PK_xquest_results_targetdecoy.xls', col_order=col_order)
