# -*- coding: utf-8 -*-
"""
Functions to read Xi processed crosslink data.
"""
import numpy as np
import pandas as pd
if __name__ in ['__main__', 'Xi']:
import HelperFunctions as hf
else:
from . import HelperFunctions as hf
def _assign_type(row):
"""
Assign mono, loop, inter and intra link
based on prot1, prot2, xlink1 and xlink2 entries
Args:
row (Series): a series or list containing prot1, prot2, xlink1, xlink2
Returns:
str or np.nan: type of cross-link (inter, intra, loop, mono)
"""
prot1, prot2, xlink1, xlink2 = row
prot1 = str(prot1)
prot2 = str(prot2)
xlink1 = str(xlink1)
xlink2 = str(xlink2)
if prot2 != 'nan' and prot1 == prot2:
t = 'intra'
elif prot2 != 'nan':
t = 'inter'
elif prot2 == 'nan' and xlink2 != 'nan':
t = 'loop'
elif prot1 != 'nan' and prot2 == 'nan' and xlink1 != 'nan':
t = 'mono'
else:
t = np.nan
return t
def _rawfile_from_source(source_str):
"""
Exctracts filename from string like
E:\julian\20180612_croco_testfiles\mgf_msconvert\20180518_JB_jb05a_l100.mgf
Args:
source_str (str): Path to a rawfile
Returns:
str: filename from path
"""
try:
return source_str.split('.')[-2].split('\\')[-1]
except AttributeError as e:
if np.isnan(float(source_str)):
return np.nan
else:
raise Exception(e)
[docs]def Read(xi_files, col_order=None, compact=False):
"""
Collects data from Xi spectrum search and returns an xtable data array.
Args:
xi_file: path or list of paths to xi 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(xi_files, list):
xi_files = [xi_files]
allData = list()
xi_dtypes = {'Scan': pd.Int64Dtype(),
'PrecoursorCharge': pd.Int64Dtype(),
'BasePeptide1': str,
'ProteinLink1': pd.Int16Dtype(),
'BasePeptide2': str,
'ProteinLink2': pd.Int16Dtype(),
'Protein1': str,
'Protein2': str,
'Start1': pd.Int32Dtype(),
'Start2': pd.Int32Dtype(),
'Link1': pd.Int16Dtype(),
'Link2': pd.Int16Dtype(),
'match score': float
}
for file in xi_files:
print('Reading xi-file: {}'.format(file))
try:
s = pd.read_csv(hf.compatible_path(file), delimiter=',', dtype=xi_dtypes)
allData.append(s)
except:
raise Exception('[xTable Read] Failed opening file: {}'.format(file))
xtable = pd.concat(allData)
### Process the data to comply to xTable format
xtable = xtable.rename(columns={'Scan': 'scanno',
'PrecoursorCharge': 'prec_ch',
'BasePeptide1': 'pepseq1',
'ProteinLink1': 'xpos1',
'BasePeptide2': 'pepseq2',
'ProteinLink2': 'xpos2',
'ModificationMasses1': 'modmass1',
'ModificationMasses2': 'modmass2',
'Modifications1': 'mod1',
'Modifications2': 'mod2',
'Protein1': 'prot1',
'Protein2': 'prot2',
'Start1': 'pos1',
'Start2': 'pos2',
'Link1': 'xlink1',
'Link2': 'xlink2',
'ModificationPositions1': 'modpos1',
'ModificationPositions2': 'modpos2',
'match score': 'score'
})
xtable['rawfile'] = xtable['Source'].apply(_rawfile_from_source)
# assign cateogries of cross-links based on identification of prot1 and prot2
xtable['type'] = xtable[['prot1', 'prot2', 'xlink1', 'xlink2']].apply(\
_assign_type, axis=1)
# 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)
if len(xtable[xtable['type'] == 'inter']) > 0:
# Reassign the type for inter xlink to inter/intra/homomultimeric
onlyInter = xtable['type'] == 'inter'
xtable.loc[onlyInter, 'type'] =\
np.vectorize(hf.categorize_inter_peptides)(xtable[onlyInter]['prot1'],
xtable[onlyInter]['pos1'],
xtable[onlyInter]['pepseq1'],
xtable[onlyInter]['prot2'],
xtable[onlyInter]['pos2'],
xtable[onlyInter]['pepseq1'])
print('[Xi Read] categorized inter peptides')
else:
print('[Xi Read] skipped inter peptide categorization')
xtable['xtype'] = np.nan
xtable['search_engine'] = 'XiSearch'
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']
xi_file = r'C:\Users\User\Documents\03_software\python\CroCo\testdata\PK\Xi\XI_results_XiVersion1.6.739.csv'
xtable = Read(xi_file)
