Pyteomics documentation v4.5.7a1

pyteomics.pepxml

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Source code for pyteomics.pepxml

"""
pepxml - pepXML file reader
===========================

Summary
-------

`pepXML <http://tools.proteomecenter.org/wiki/index.php?title=Formats:pepXML>`_
was the first widely accepted format for proteomics search engines' output.
Even though it is to be replaced by a community standard
`mzIdentML <http://www.psidev.info/index.php?q=node/454>`_, it is still used
commonly.

This module provides minimalistic infrastructure for access to data stored in
pepXML files. The most important function is :py:func:`read`, which
reads peptide-spectum matches and related information and saves them into
human-readable dicts. This function relies on the terminology of the underlying
`lxml library <http://lxml.de/>`_.

Data access
-----------

  :py:class:`PepXML` - a class representing a single pepXML file.
  Other data access functions use this class internally.

  :py:func:`read` - iterate through peptide-spectrum matches in a pepXML
  file. Data for a single spectrum are converted to an easy-to-use dict.

  :py:func:`chain` - read multiple files at once.

  :py:func:`chain.from_iterable` - read multiple files at once, using an
  iterable of files.

  :py:func:`DataFrame` - read pepXML files into a :py:class:`pandas.DataFrame`.

Target-decoy approach
---------------------

  :py:func:`filter` - filter PSMs from a chain of pepXML files to a specific FDR
  using TDA.

  :py:func:`filter.chain` - chain a series of filters applied independently to
  several files.

  :py:func:`filter.chain.from_iterable` - chain a series of filters applied
  independently to an iterable of files.

  :py:func:`filter_df` - filter pepXML files and return a :py:class:`pandas.DataFrame`.

  :py:func:`fdr` - estimate the false discovery rate of a PSM set using the
  target-decoy approach.

  :py:func:`qvalues` - get an array of scores and local FDR values for a PSM
  set using the target-decoy approach.

  :py:func:`is_decoy` - determine whether a PSM is decoy or not.

Miscellaneous
-------------

  :py:func:`roc_curve` - get a receiver-operator curve (min PeptideProphet
  probability in a sample vs. false discovery rate) of PeptideProphet analysis.

Deprecated functions
--------------------

  :py:func:`iterfind` - iterate over elements in a pepXML file.
  You can just call the corresponding method of the :py:class:`PepXML`
  object.

  :py:func:`version_info` - get information about pepXML version and schema.
  You can just read the corresponding attribute of the :py:class:`PepXML`
  object.

Dependencies
------------

This module requires :py:mod:`lxml`.

-------------------------------------------------------------------------------
"""

#   Copyright 2012 Anton Goloborodko, Lev Levitsky
#
#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
#   You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
#   Unless required by applicable law or agreed to in writing, software
#   distributed under the License is distributed on an "AS IS" BASIS,
#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#   See the License for the specific language governing permissions and
#   limitations under the License.

from lxml import etree
from . import xml, auxiliary as aux, _schema_defaults


[docs]class PepXML(xml.MultiProcessingXML, xml.IndexSavingXML): """Parser class for pepXML files.""" file_format = 'pepXML' _root_element = 'msms_pipeline_analysis' _default_schema = _schema_defaults._pepxml_schema_defaults _default_version = '1.15' _default_iter_tag = 'spectrum_query' _indexed_tags = {'spectrum_query'} _indexed_tag_keys = {'spectrum_query': 'spectrum'} _default_id_attr = 'spectrum' _structures_to_flatten = {'search_score_summary', 'modification_info'} # attributes which contain unconverted values _convert_items = {'float': {'calc_neutral_pep_mass', 'massdiff', 'probability', 'variable', 'static'}, 'int': {'start_scan', 'end_scan', 'index', 'num_matched_peptides'}, 'bool': {'is_rejected'}, 'floatarray': {'all_ntt_prob'}}.items() def _get_info_smart(self, element, **kwargs): """Extract the info in a smart way depending on the element type""" try: name = kwargs.pop('ename') except KeyError: name = xml._local_name(element) rec = kwargs.pop('recursive', None) if name == self._root_element: info = self._get_info(element, ename=name, recursive=(rec if rec is not None else False), **kwargs) else: info = self._get_info(element, ename=name, recursive=(rec if rec is not None else True), **kwargs) def safe_float(s): try: return float(s) except ValueError: if s.startswith('+-0'): return 0 return s converters = {'float': safe_float, 'int': int, 'bool': lambda x: x.lower() in {'1', 'true'}, 'floatarray': lambda x: list(map(float, x[1:-1].split(',')))} for k, v in dict(info).items(): for t, s in self._convert_items: if k in s: del info[k] info[k] = converters[t](v) for k in {'search_score', 'parameter'}: if k in info and isinstance(info[k], list) and all( isinstance(x, dict) and len(x) == 1 for x in info[k]): scores = {} for score in info[k]: name, value = score.popitem() try: scores[name] = float(value) except ValueError: scores[name] = value info[k] = scores if 'search_result' in info and len(info['search_result']) == 1: info.update(info['search_result'][0]) del info['search_result'] if 'protein' in info and 'peptide' in info: info['proteins'] = [{'protein': info.pop('protein'), 'protein_descr': info.pop('protein_descr', None)}] for add_key in {'peptide_prev_aa', 'peptide_next_aa', 'protein_mw'}: if add_key in info: info['proteins'][0][add_key] = info.pop(add_key) info['proteins'][0]['num_tol_term'] = info.pop('num_tol_term', 0) if 'alternative_protein' in info: info['proteins'].extend(info['alternative_protein']) del info['alternative_protein'] if 'peptide' in info and not 'modified_peptide' in info: info['modified_peptide'] = info['peptide'] if 'peptide' in info: info['modifications'] = info.pop('mod_aminoacid_mass', []) if 'mod_nterm_mass' in info: info['modifications'].insert(0, {'position': 0, 'mass': float(info.pop('mod_nterm_mass'))}) if 'mod_cterm_mass' in info: info['modifications'].append({'position': 1 + len(info['peptide']), 'mass': float(info.pop('mod_cterm_mass'))}) if 'modified_peptide' in info and info['modified_peptide'] == info.get( 'peptide'): if not info.get('modifications'): info['modifications'] = [] else: mp = info['modified_peptide'] for mod in sorted(info['modifications'], key=lambda m: m['position'], reverse=True): if mod['position'] not in {0, 1+len(info['peptide'])}: p = mod['position'] mp = mp[:p] + '[{}]'.format(int(mod['mass'])) + mp[p:] info['modified_peptide'] = mp if 'search_hit' in info: info['search_hit'].sort(key=lambda x: x['hit_rank']) return info
[docs]def read(source, read_schema=False, iterative=True, **kwargs): """Parse `source` and iterate through peptide-spectrum matches. Parameters ---------- source : str or file A path to a target pepXML file or the file object itself. read_schema : bool, optional If :py:const:`True`, attempt to extract information from the XML schema mentioned in the pepXML header. Otherwise, use default parameters. Not recommended without Internet connection or if you don't like to get the related warnings. iterative : bool, optional Defines whether iterative parsing should be used. It helps reduce memory usage at almost the same parsing speed. Default is :py:const:`True`. Returns ------- out : PepXML An iterator over dicts with PSM properties. """ return PepXML(source, read_schema=read_schema, iterative=iterative)
[docs]def iterfind(source, path, **kwargs): """Parse `source` and yield info on elements with specified local name or by specified "XPath". .. note:: This function is provided for backward compatibility only. If you do multiple :py:func:`iterfind` calls on one file, you should create an :py:class:`PepXML` object and use its :py:meth:`!iterfind` method. Parameters ---------- source : str or file File name or file-like object. path : str Element name or XPath-like expression. Only local names separated with slashes are accepted. An asterisk (`*`) means any element. You can specify a single condition in the end, such as: ``"/path/to/element[some_value>1.5]"`` Note: you can do much more powerful filtering using plain Python. The path can be absolute or "free". Please don't specify namespaces. recursive : bool, keyword only, optional If :py:const:`False`, subelements will not be processed when extracting info from elements. Default is :py:const:`True`. iterative : bool, keyword only, optional Specifies whether iterative XML parsing should be used. Iterative parsing significantly reduces memory usage and may be just a little slower. When `retrieve_refs` is :py:const:`True`, however, it is highly recommended to disable iterative parsing if possible. Default value is :py:const:`True`. read_schema : bool, keyword only, optional If :py:const:`True`, attempt to extract information from the XML schema mentioned in the mzIdentML header. Otherwise, use default parameters. Not recommended without Internet connection or if you don't like to get the related warnings. Returns ------- out : iterator """ return PepXML(source, **kwargs).iterfind(path, **kwargs)
version_info = xml._make_version_info(PepXML)
[docs]def roc_curve(source): """Parse source and return a ROC curve for peptideprophet analysis. Parameters ---------- source : str or file A path to a target pepXML file or the file object itself. Returns ------- out : list A list of ROC points. """ parser = etree.XMLParser(remove_comments=True, ns_clean=True) tree = etree.parse(source, parser=parser) roc_curve = [] for roc_error_data in tree.xpath( "/*[local-name()='msms_pipeline_analysis'] \ //*[local-name()='analysis_summary' and @analysis='peptideprophet'] \ //*[local-name()='peptideprophet_summary'] \ //*[local-name()='roc_error_data']"): for element in roc_error_data.xpath("*[local-name()='roc_data_point' or local-name()='error_point']"): data_point = dict(element.attrib) for key in data_point: data_point[key] = float(data_point[key]) data_point["charge"] = roc_error_data.attrib["charge"] data_point["tag"] = etree.QName(element).localname roc_curve.append(data_point) return roc_curve
# chain = aux._make_chain(read, 'read') chain = aux.ChainBase._make_chain(read) def _is_decoy_prefix(psm, prefix='DECOY_'): """Given a PSM dict, return :py:const:`True` if all protein names for the PSM start with ``prefix``, and :py:const:`False` otherwise. This function might not work for some pepXML flavours. Use the source to get the idea and suit it to your needs. Parameters ---------- psm : dict A dict, as yielded by :py:func:`read`. prefix : str, optional A prefix used to mark decoy proteins. Default is `'DECOY_'`. Returns ------- out : bool """ return all(protein['protein'].startswith(prefix) for protein in psm['search_hit'][0]['proteins']) def _is_decoy_suffix(psm, suffix='_DECOY'): return all(protein['protein'].endswith(suffix) for protein in psm['search_hit'][0]['proteins']) is_decoy = _is_decoy_prefix fdr = aux._make_fdr(_is_decoy_prefix, _is_decoy_suffix) _key = lambda x: min(sh['search_score']['expect'] for sh in x['search_hit']) qvalues = aux._make_qvalues(chain, _is_decoy_prefix, _is_decoy_suffix, _key) filter = aux._make_filter(chain, _is_decoy_prefix, _is_decoy_suffix, _key, qvalues) filter.chain = aux._make_chain(filter, 'filter', True)
[docs]def DataFrame(*args, **kwargs): """Read pepXML output files into a :py:class:`pandas.DataFrame`. Requires :py:mod:`pandas`. Parameters ---------- *args pepXML file names or objects. Passed to :py:func:`chain`. **kwargs Passed to :py:func:`chain`. sep : str or None, keyword only, optional Some values related to PSMs (such as protein information) are variable-length lists. If `sep` is a :py:class:`str`, they will be packed into single string using this delimiter. If `sep` is :py:const:`None`, they are kept as lists. Default is :py:const:`None`. recursive : bool, keyword only, optional If :py:const:`False`, subelements will not be processed when extracting info from elements. Default is :py:const:`True`. iterative : bool, keyword only, optional Specifies whether iterative XML parsing should be used. Iterative parsing significantly reduces memory usage and may be just a little slower. When `retrieve_refs` is :py:const:`True`, however, it is highly recommended to disable iterative parsing if possible. Default value is :py:const:`True`. read_schema : bool, keyword only, optional If :py:const:`True`, attempt to extract information from the XML schema mentioned in the mzIdentML header. Otherwise, use default parameters. Not recommended without Internet connection or if you don't like to get the related warnings. pd_kwargs : dict, optional Keyword arguments passed to the :py:class:`pandas.DataFrame` constructor. Returns ------- out : pandas.DataFrame """ import pandas as pd kwargs = kwargs.copy() sep = kwargs.pop('sep', None) pd_kwargs = kwargs.pop('pd_kwargs', {}) def gen_items(): with chain(*args, **kwargs) as f: for item in f: info = {} for k, v in item.items(): if isinstance(v, (str, int, float)): info[k] = v if 'search_hit' in item: sh = item['search_hit'][0] proteins = sh.pop('proteins') prot_dict = {} for p in proteins: for k in p: prot_dict[k] = [] for p in proteins: for k, v in prot_dict.items(): v.append(p.get(k)) if sep is None: info.update(prot_dict) else: for k, v in prot_dict.items(): info[k] = sep.join(str(val) if val is not None else '' for val in v) info.update(sh.pop('search_score')) mods = sh.pop('modifications', []) formatted_mods = ['{0[mass]:.3f}@{0[position]}'.format(x) for x in mods] if sep is not None: info['modifications'] = sep.join(formatted_mods) else: info['modifications'] = formatted_mods for k, v in sh.items(): if isinstance(v, (str, int, float)): info[k] = v if 'analysis_result' in sh: for ar in sh['analysis_result']: if ar['analysis'] == 'peptideprophet': try: info.update(ar['peptideprophet_result']['parameter']) except KeyError: pass info['peptideprophet_probability'] = ar['peptideprophet_result']['probability'] info['peptideprophet_ntt_prob'] = ar['peptideprophet_result']['all_ntt_prob'] elif ar['analysis'] == 'interprophet': info.update(ar['interprophet_result']['parameter']) info['interprophet_probability'] = ar['interprophet_result']['probability'] info['interprophet_ntt_prob'] = ar['interprophet_result']['all_ntt_prob'] yield info return pd.DataFrame(gen_items(), **pd_kwargs)
[docs]def filter_df(*args, **kwargs): """Read pepXML files or DataFrames and return a :py:class:`DataFrame` with filtered PSMs. Positional arguments can be pepXML files or DataFrames. Keyword parameter `fdr` is also required. Other parameters are optional. Requires :py:mod:`pandas`. Parameters ---------- positional args pepXML file names, file objects, or DataFrames. Passed to :py:func:`DataFrame`. fdr : float, keyword only, 0 <= fdr <= 1 Desired FDR level. key : str / iterable / callable, keyword only, optional PSM score. Default is 'expect'. is_decoy : str / iterable / callable, keyword only, optional Default is to check if all strings in the "protein" column start with `'DECOY_'`. sep : str or None, keyword only, optional Some values related to PSMs (such as protein information) are variable-length lists. If `sep` is a :py:class:`str`, they will be packed into single string using this delimiter. If `sep` is :py:const:`None`, they are kept as lists. Default is :py:const:`None`. reverse : bool, keyword only, optional If :py:const:`True`, then PSMs are sorted in descending order, i.e. the value of the key function is higher for better PSMs. Default is :py:const:`False`. decoy_prefix : str, optional If the default `is_decoy` function works for you, this parameter specifies which protein name prefix to use to detect decoy matches. If you provide your own `is_decoy`, or if you specify `decoy_suffix`, this parameter has no effect. Default is `"DECOY_"`. decoy_suffix : str, optional If the default `is_decoy` function works for you, this parameter specifies which protein name suffix to use to detect decoy matches. If you provide your own `is_decoy`, this parameter has no effect. Mutually exclusive with `decoy_prefix`. remove_decoy : bool, keyword only, optional Defines whether decoy matches should be removed from the output. Default is :py:const:`True`. .. note:: If set to :py:const:`False`, then by default the decoy PSMs will be taken into account when estimating FDR. Refer to the documentation of :py:func:`fdr` for math; basically, if `remove_decoy` is :py:const:`True`, then formula 1 is used to control output FDR, otherwise it's formula 2. This can be changed by overriding the `formula` argument. formula : int, keyword only, optional Can be either 1 or 2, defines which formula should be used for FDR estimation. Default is 1 if `remove_decoy` is :py:const:`True`, else 2 (see :py:func:`fdr` for definitions). ratio : float, keyword only, optional The size ratio between the decoy and target databases. Default is 1. In theory, the "size" of the database is the number of theoretical peptides eligible for assignment to spectra that are produced by *in silico* cleavage of that database. correction : int or float, keyword only, optional Possible values are 0, 1 and 2, or floating point numbers between 0 and 1. 0 (default): no correction; 1: enable "+1" correction. This accounts for the probability that a false positive scores better than the first excluded decoy PSM; 2: this also corrects that probability for finite size of the sample, so the correction will be slightly less than "+1". If a floating point number is given, then instead of the expectation value for the number of false PSMs, the confidence value is used. The value of `correction` is then interpreted as desired confidence level. E.g., if correction=0.95, then the calculated q-values do not exceed the "real" q-values with 95% probability. See `this paper <http://dx.doi.org/10.1021/acs.jproteome.6b00144>`_ for further explanation. pep : callable / array-like / iterable / str, keyword only, optional If callable, a function used to determine the posterior error probability (PEP). Should accept exactly one argument (PSM) and return a float. If array-like, should contain float values for all given PSMs. If string, it is used as a field name (PSMs must be in a record array or a :py:class:`DataFrame`). .. note:: If this parameter is given, then PEP values will be used to calculate q-values. Otherwise, decoy PSMs will be used instead. This option conflicts with: `is_decoy`, `remove_decoy`, `formula`, `ratio`, `correction`. `key` can still be provided. Without `key`, PSMs will be sorted by PEP. q_label : str, optional Field name for q-value in the output. Default is ``'q'``. score_label : str, optional Field name for score in the output. Default is ``'score'``. decoy_label : str, optional Field name for the decoy flag in the output. Default is ``'is decoy'``. pep_label : str, optional Field name for PEP in the output. Default is ``'PEP'``. Returns ------- out : pandas.DataFrame """ import pandas as pd sep = kwargs.get('sep') kwargs.setdefault('key', 'expect') if all(isinstance(arg, pd.DataFrame) for arg in args): if len(args) > 1: df = pd.concat(args) else: df = args[0] else: read_kw = {k: kwargs.pop(k) for k in ['iterative', 'read_schema', 'sep', 'pd_kwargs'] if k in kwargs} df = DataFrame(*args, **read_kw) if 'is_decoy' not in kwargs: if sep is not None: if 'decoy_suffix' in kwargs: kwargs['is_decoy'] = df['protein'].str.split(';').apply( lambda s: all(x.endswith(kwargs['decoy_suffix']) for x in s)) else: kwargs['is_decoy'] = df['protein'].str.split(';').apply( lambda s: all(x.startswith(kwargs.get('decoy_prefix', 'DECOY_')) for x in s)) else: if 'decoy_suffix' in kwargs: kwargs['is_decoy'] = df['protein'].apply( lambda s: all(x.endswith(kwargs['decoy_suffix']) for x in s)) else: kwargs['is_decoy'] = df['protein'].apply( lambda s: all(x.startswith(kwargs.get('decoy_prefix', 'DECOY_')) for x in s)) return aux.filter(df, **kwargs)

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