Pyteomics documentation v3.4.2

pepxml - pepXML file reader

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pepxml - pepXML file reader

Summary

pepXML was the first widely accepted format for proteomics search engines’ output. Even though it is to be replaced by a community standard mzIdentML, it is still used commonly.

This module provides minimalistic infrastructure for access to data stored in pepXML files. The most important function is 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.

Data access

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

read() - iterate through peptide-spectrum matches in a pepXML file. Data for a single spectrum are converted to an easy-to-use dict.

chain() - read multiple files at once.

chain.from_iterable() - read multiple files at once, using an iterable of files.

DataFrame() - read pepXML files into a pandas.DataFrame.

Target-decoy approach

filter() - filter PSMs from a chain of pepXML files to a specific FDR using TDA.

filter.chain() - chain a series of filters applied independently to several files.

filter.chain.from_iterable() - chain a series of filters applied independently to an iterable of files.

filter_df() - filter pepXML files and return a pandas.DataFrame.

fdr() - estimate the false discovery rate of a PSM set using the target-decoy approach.

qvalues() - get an array of scores and local FDR values for a PSM set using the target-decoy approach.

is_decoy() - determine whether a PSM is decoy or not.

Miscellaneous

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

Deprecated functions

iterfind() - iterate over elements in a pepXML file. You can just call the corresponding method of the PepXML object.

version_info() - get information about pepXML version and schema. You can just read the corresponding attribute of the PepXML object.

Dependencies

This module requires lxml.


pyteomics.pepxml.chain(*args, **kwargs)

Chain read() for several files. Positional arguments should be file names or file objects. Keyword arguments are passed to the read() function.

chain.from_iterable(files, **kwargs)

Chain read() for several files. Keyword arguments are passed to the read() function.

Parameters:

files : iterable

Iterable of file names or file objects.

pyteomics.pepxml.filter(*args, **kwargs)

Read args and yield only the PSMs that form a set with estimated false discovery rate (FDR) not exceeding fdr.

Requires numpy and, optionally, pandas.

Parameters:

positional args : file or str

Files to read PSMs from. All positional arguments are treated as files. The rest of the arguments must be named.

fdr : float, keyword only, 0 <= fdr <= 1

Desired FDR level.

key : callable / array-like / iterable / str, keyword only, optional

A function used for sorting of PSMs. Should accept exactly one argument (PSM) and return a number (the smaller the better). The default is a function that tries to extract e-value from the PSM.

Warning

The default function may not work with your files, because format flavours are diverse.

reverse : bool, keyword only, optional

If True, then PSMs are sorted in descending order, i.e. the value of the key function is higher for better PSMs. Default is False.

is_decoy : callable / array-like / iterable / str, keyword only, optional

A function used to determine if the PSM is decoy or not. Should accept exactly one argument (PSM) and return a truthy value if the PSM should be considered decoy.

Warning

The default function may not work with your files, because format flavours are diverse.

remove_decoy : bool, keyword only, optional

Defines whether decoy matches should be removed from the output. Default is True.

Note

If set to False, then by default the decoy PSMs will be taken into account when estimating FDR. Refer to the documentation of fdr() for math; basically, if remove_decoy is 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 True, else 2 (see 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. Default is 0 (no correction); 1 accounts for the probability that a false positive scores better than the first excluded decoy PSM; 2 also corrects that probability for finite size of the sample. 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 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 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.

full_output : bool, keyword only, optional

If True, then an array of PSM objects is returned. Otherwise, an iterator / context manager object is returned, and the files are parsed twice. This saves some RAM, but is ~2x slower. Default is True.

Note

The name for the parameter comes from the fact that it is internally passed to qvalues().

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'.

**kwargs : passed to the chain() function.

Returns:

out : iterator or numpy.ndarray or pandas.DataFrame

filter.chain(*files, **kwargs)

Chain filter() for several files. Positional arguments should be file names or file objects. Keyword arguments are passed to the filter() function.

filter.chain.from_iterable(*files, **kwargs)

Chain filter() for several files. Keyword arguments are passed to the filter() function.

Parameters:

files : iterable

Iterable of file names or file objects.

pyteomics.pepxml.version_info(source)

Provide version information about the pepXML file.

Note

This function is provided for backward compatibility only. It simply creates an PepXML instance and returns its version_info attribute.

Parameters:

source : str or file

File name or file-like object.

Returns:

out : tuple

A (version, schema URL) tuple, both elements are strings or None.

pyteomics.pepxml.iterfind(source, path, **kwargs)[source]

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 iterfind() calls on one file, you should create an PepXML object and use its 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, optional

If False, subelements will not be processed when extracting info from elements. Default is True.

iterative : bool, 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 True, however, it is highly recommended to disable iterative parsing if possible. Default value is True.

read_schema : bool, optional

If True, attempt to extract information from the XML schema mentioned in the mzIdentML header (default). Otherwise, use default parameters. Disable this to avoid waiting on slow network connections or if you don’t like to get the related warnings.

Returns:

out : iterator

pyteomics.pepxml.fdr(psms=None, formula=1, is_decoy=<function is_decoy>, ratio=1, correction=0, pep=None)

Estimate FDR of a data set using TDA or given PEP values. Two formulas can be used. The first one (default) is:

The second formula is:

Note

This function is less versatile than qvalues(). To obtain FDR, you can call qvalues() and take the last q-value. This function can be used (with correction = 0 or 1) when numpy is not available.

Parameters:

psms : iterable, optional

An iterable of PSMs, e.g. as returned by read(). Not needed if is_decoy is an iterable.

formula : int, optional

Can be either 1 or 2, defines which formula should be used for FDR estimation. Default is 1.

is_decoy : callable, iterable, or str, optional

If callable, should accept exactly one argument (PSM) and return a truthy value if the PSM is considered decoy. Default is is_decoy(). 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 pandas.DataFrame).

Warning

The default function may not work with your files, because format flavours are diverse.

pep : callable, iterable, or str, 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 pandas.DataFrame).

Note

If this parameter is given, then PEP values will be used to calculate FDR. Otherwise, decoy PSMs will be used instead. This option conflicts with: is_decoy, formula, ratio, correction.

ratio : float, 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, optional

Possible values are 0, 1 and 2, or floating point numbers between 0 and 1. Default is 0 (no correction); 1 accounts for the probability that a false positive scores better than the first excluded decoy PSM; 2 also corrects that probability for finite size of the sample. 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 for further explanation.

Note

Requires numpy, if correction is a float or 2.

Note

Correction is only needed if the PSM set at hand was obtained using TDA filtering based on decoy counting (as done by using filter() without correction).

Returns:

out : float

The estimation of FDR, (roughly) between 0 and 1.

pyteomics.pepxml.qvalues(*args, **kwargs)

Read args and return a NumPy array with scores and q-values. q-values are calculated either using TDA or based on provided values of PEP.

Requires numpy (and optionally pandas).

Parameters:

positional args : file or str

Files to read PSMs from. All positional arguments are treated as files. The rest of the arguments must be named.

key : callable / array-like / iterable / str, keyword only, optional

If callable, a function used for sorting of PSMs. Should accept exactly one argument (PSM) and return a number (the smaller the better). If array-like, should contain scores for all given PSMs. If string, it is used as a field name (PSMs must be in a record array or a DataFrame).

Warning

The default function may not work with your files, because format flavours are diverse.

reverse : bool, keyword only, optional

If True, then PSMs are sorted in descending order, i.e. the value of the key function is higher for better PSMs. Default is False.

is_decoy : callable / array-like / iterable / str, keyword only, optional

If callable, a function used to determine if the PSM is decoy or not. Should accept exactly one argument (PSM) and return a truthy value if the PSM should be considered decoy. If array-like, should contain boolean values for all given PSMs. If string, it is used as a field name (PSMs must be in a record array or a DataFrame).

Warning

The default function may not work with your files, because format flavours are diverse.

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 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.

remove_decoy : bool, keyword only, optional

Defines whether decoy matches should be removed from the output. Default is False.

Note

If set to False, then by default the decoy PSMs will be taken into account when estimating FDR. Refer to the documentation of fdr() for math; basically, if remove_decoy is 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 True, else 2 (see 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. Default is 0 (no correction); 1 accounts for the probability that a false positive scores better than the first excluded decoy PSM; 2 also corrects that probability for finite size of the sample. 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 for further explanation.

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'.

full_output : bool, keyword only, optional

If True, then the returned array has PSM objects along with scores and q-values. Default is False.

**kwargs : passed to the chain() function.

Returns:

out : numpy.ndarray

A sorted array of records with the following fields:

  • ‘score’: np.float64
  • ‘is decoy’: np.bool_
  • ‘q’: np.float64
  • ‘psm’: np.object_ (if full_output is True)
pyteomics.pepxml.DataFrame(*args, **kwargs)[source]

Read pepXML output files into a pandas.DataFrame.

Requires pandas.

Parameters:

*args, **kwargs : passed to chain()

sep : str or None, optional

Some values related to PSMs (such as protein information) are variable-length lists. If sep is a str, they will be packed into single string using this delimiter. If sep is None, they are kept as lists. Default is None.

Returns:

out : pandas.DataFrame

class pyteomics.pepxml.PepXML(source, read_schema=True, iterative=True, build_id_cache=False, **kwargs)[source]

Bases: pyteomics.xml.XML

Parser class for pepXML files.

Methods

build_id_cache(*args, **kwargs) Construct a cache for each element in the document, indexed by id
build_tree(*args, **kwargs) Build and store the ElementTree instance
clear_id_cache() Clear the element ID cache
clear_tree() Remove the saved ElementTree.
get_by_id(*args, **kwargs) Parse the file and return the element with id attribute equal to elem_id.
iterfind(*args, **kwargs) Parse the XML and yield info on elements with specified local name or by specified “XPath”.
next()
reset()
__init__(source, read_schema=True, iterative=True, build_id_cache=False, **kwargs)

Create an XML parser object.

Parameters:

source : str or file

File name or file-like object corresponding to an XML file.

read_schema : bool, optional

Defines whether schema file referenced in the file header should be used to extract information about value conversion. Default is True.

iterative : bool, optional

Defines whether an ElementTree object should be constructed and stored on the instance or if iterative parsing should be used instead. Iterative parsing keeps the memory usage low for large XML files. Default is True.

build_id_cache : bool, optional

Defines whether a dictionary mapping IDs to XML tree elements should be built and stored on the instance. It is used in XML.get_by_id(), e.g. when using pyteomics.mzid.MzIdentML with retrieve_refs=True.

build_id_cache(*args, **kwargs)

Construct a cache for each element in the document, indexed by id attribute

build_tree(*args, **kwargs)

Build and store the ElementTree instance for the underlying file

clear_id_cache()

Clear the element ID cache

clear_tree()

Remove the saved ElementTree.

get_by_id(*args, **kwargs)

Parse the file and return the element with id attribute equal to elem_id. Returns None if no such element is found.

Parameters:

elem_id : str

The value of the id attribute to match.

Returns:

out : dict or None

iterfind(*args, **kwargs)

Parse the XML and yield info on elements with specified local name or by specified “XPath”.

Parameters:

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.

**kwargs : passed to self._get_info_smart().

Returns:

out : iterator

pyteomics.pepxml.filter_df(*args, **kwargs)[source]

Read pepXML files or DataFrames and return a DataFrame with filtered PSMs. Positional arguments can be pepXML files or DataFrames.

Requires pandas.

Parameters:

key : str / iterable / callable, optional

Default is ‘expect’.

is_decoy : str / iterable / callable, optional

Default is to check if all strings in the “protein” column start with ‘DECOY_’

*args, **kwargs : passed to auxiliary.filter() and/or DataFrame().

Returns:

out : pandas.DataFrame

pyteomics.pepxml.is_decoy(psm, prefix=’DECOY_’)[source]

Given a PSM dict, return True if all protein names for the PSM start with prefix, and 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 read().

prefix : str, optional

A prefix used to mark decoy proteins. Default is ‘DECOY_’.

Returns:

out : bool

pyteomics.pepxml.iterfind(source, path, **kwargs)[source]

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 iterfind() calls on one file, you should create an PepXML object and use its 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, optional

If False, subelements will not be processed when extracting info from elements. Default is True.

iterative : bool, 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 True, however, it is highly recommended to disable iterative parsing if possible. Default value is True.

read_schema : bool, optional

If True, attempt to extract information from the XML schema mentioned in the mzIdentML header (default). Otherwise, use default parameters. Disable this to avoid waiting on slow network connections or if you don’t like to get the related warnings.

Returns:

out : iterator

pyteomics.pepxml.read(source, read_schema=True, iterative=True, **kwargs)[source]

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 True, attempt to extract information from the XML schema mentioned in the pepXML header (default). Otherwise, use default parameters. Disable this to avoid waiting on slow network connections 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 True.

Returns:

out : PepXML

An iterator over dicts with PSM properties.

pyteomics.pepxml.roc_curve(source)[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, sorted by ascending min prob.

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