Chain TandemXML for several sources into a single iterable. Positional arguments should be sources like file names or file objects. Keyword arguments are passed to the TandemXML function.

Parameters:

• sources (Iterable) – Sources for creating new sequences from, such as paths or file-like objects

• kwargs (Mapping) – Additional arguments used to instantiate each sequence

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

Parameters:

files – Iterable of file names or file objects.

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:

• args (positional) – 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

Return type:

iterator or numpy.ndarray or pandas.DataFrame

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

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

Parameters:

files – Iterable of file names or file objects.

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:

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

Return type:

float

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:

• args (positional) – 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 – 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)

Return type:

numpy.ndarray

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

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.

Returns:

out

Return type:

iterator

Read X!Tandem output files into a pandas.DataFrame.

Requires pandas.

Parameters:

• 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.

• pd_kwargs (dict, optional) – Keyword arguments passed to the pandas.DataFrame constructor.

• *args – Passed to chain().

• **kwargs – Passed to chain().

Returns:

out

Return type:

pandas.DataFrame

Read X!Tandem output files or DataFrames and return a DataFrame with filtered PSMs. Positional arguments can be X!Tandem output 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 – Passed to auxiliary.filter() and/or DataFrame().

• **kwargs – Passed to auxiliary.filter() and/or DataFrame().

Returns:

out

Return type:

pandas.DataFrame

Given a PSM dict, return True if all protein names for the PSM start with prefix, and False otherwise.

Parameters:

• psm (dict) – A dict, as yielded by read().

• prefix (str, optional) – A prefix used to mark decoy proteins. Default is ‘DECOY_’.

Returns:

out

Return type:

bool

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

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.

Returns:

out

Return type:

iterator

Parse source and iterate through peptide-spectrum matches.

Parameters:

• source (str or file) – A path to a target X!Tandem output file or the file object itself.

• 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 – An iterator over dicts with PSM properties.

Return type:

iterator