Pyteomics documentation v3.5.1

protxml - parsing of ProteinProphet output files

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protxml - parsing of ProteinProphet output files

Summary

protXML is the output format of the ProteinProphet software. It contains information about identified proteins and their statistical significance.

This module provides minimalistic infrastructure for access to data stored in protXML files. The central class is ProtXML, which reads protein entries and related information and saves them into Python dicts.

Data access

ProtXML - a class representing a single protXML file. Other data access functions use this class internally.

read() - iterate through peptide-spectrum matches in a protXML file. Calling the function is synonymous to instantiating the ProtXML class.

chain() - read multiple files at once.

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

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

Target-decoy approach

filter() - filter protein groups from a chain of protXML 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 protXML files and return a pandas.DataFrame.

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

qvalues() - get an array of scores and q values for protein groups using the target-decoy approach.

is_decoy() - determine whether a protein group is decoy or not. This function may not suit your use case.

Dependencies

This module requres lxml.


pyteomics.protxml.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.

files : iterable
Iterable of file names or file objects.
pyteomics.protxml.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.

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 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.

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 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.

files : iterable
Iterable of file names or file objects.
pyteomics.protxml.fdr(psms=None, formula=1, is_decoy=None, ratio=1, correction=0, pep=None, decoy_prefix='DECOY_', decoy_suffix=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.

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.

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.

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 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.protxml.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.

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.

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.

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 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.protxml.DataFrame(*args, **kwargs)[source]

Read protXML output files into a pandas.DataFrame.

Note

Rows in the DataFrame correspond to individual proteins, not protein groups.

Requires pandas.

Parameters:
*args, **kwargs : passed to chain()
sep : str or None, optional

Some values related to protein groups 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.

pd_kwargs : dict, optional

Keyword arguments passed to the pandas.DataFrame constructor.

Returns:
out : pandas.DataFrame
class pyteomics.protxml.ProtXML(source, read_schema=False, iterative=True, build_id_cache=False, **kwargs)[source]

Bases: pyteomics.xml.XML

Parser class for protXML files.

Methods

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.
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=False, 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 False.

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.

huge_tree : bool, optional

This option is passed to the lxml parser and defines whether security checks for XML tree depth and node size should be disabled. Default is False. Enable this option for trusted files to avoid XMLSyntaxError exceptions (e.g. XMLSyntaxError: xmlSAX2Characters: huge text node).

skip_empty_cvparam_values : bool, optional

Warning

This parameter affects the format of the produced dictionaries.

By default, when parsing cvParam elements, “value” attributes with empty values are not treated differently from others. When this parameter is set to True, these empty values are flattened. You can enable this to obtain the same output structure regardless of the presence of an empty “value”. Default is False.

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
reset()

Resets the iterator to its initial state.

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

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

Note

Rows in the DataFrame correspond to individual proteins, not protein groups.

Requires pandas.

Parameters:
key : str / iterable / callable, optional

Default is ‘probability’.

is_decoy : str / iterable / callable, optional

Default is to check that “protein_name” starts with ‘DECOY_’.

reverse : bool, optional

Should be True if higher score is better. Default is True (because the default key is ‘probability’).

*args, **kwargs : passed to auxiliary.filter() and/or DataFrame().
Returns:
out : pandas.DataFrame
pyteomics.protxml.is_decoy(pg, prefix='DECOY_')

Determine if a protein group should be considered decoy.

This function checks that all protein names in a group start with prefix. You may need to provide your own function for correct filtering and FDR estimation.

Parameters:
pg : dict

A protein group dict produced by the ProtXML parser.

prefix : str, optional

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

Returns:
out : bool
pyteomics.protxml.read(source, read_schema=False, iterative=True, **kwargs)[source]

Parse source and iterate through protein groups.

Parameters:
source : str or file

A path to a target protXML file or the file object itself.

read_schema : bool, optional

If True, attempt to extract information from the XML schema mentioned in the protXML 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 True.

Returns:
out : ProtXML

An iterator over dicts with protein group properties.

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