massdash.loaders.access.GenericResultsAccess

class massdash.loaders.access.GenericResultsAccess(filename: str, verbose: bool = False)

Bases: ABC

Abstract class for accessing results from a generic results file.

getExperimentSummary() → DataFrame

Get a summary of the experiment

Returns:

DataFrame containing the experiment summary. Each row is a run and the columns are the run metadata (# Precursors, # Pepitdes, # Proteins, Software)’

Return type:

DataFrame

abstract getIdentifiedPeptides(runname: str, qvalue: float, run: str | None = None) → set | Dict[str, set]

Get the identified peptides at a certain q-value.

Parameters:

  • qvalue – (float) The q-value threshold for identification

  • run – (str) The run name for which to get the identified peptides, if None, get for all runs

Returns:

The identified peptides across all runs (Dict[str, set]) or for a single run (set)

abstract getIdentifiedPrecursorIntensities(**kwargs) → DataFrame

Get the identified precursor intensities at a certain q-value.

Parameters:

**kwargs (dict) – Additional arguments to be passed to the getIdentifiedPrecursor function

Returns:

Precursor, runName, Intensity) or for a single run (DataFrame with columns: Precursor, Intensity)

Return type:

The identified precursor intensities across all runs (DataFrame with columns

abstract getIdentifiedPrecursors(qvalue: float = 0.01, run: str | None = None, precursorLevel: bool = False) → set | Dict[str, set]

Get the identified precursors at a certain q-value.

Parameters:

  • qvalue – (float) The q-value threshold for identification

  • run – (str) The run name for which to get the identified precursors, if None, get for all runs

  • precursorLevel – (bool) If True, return the precursor level identification, else return the peptide level identification

Returns:

The identified precursors across all runs (Dict[str, set]) or for a single run (set)

abstract getIdentifiedProteins(qvalue: float, run: str | None = None) → set | Dict[str, set]

Get the identified proteins at a certain q-value.

Parameters:

  • qvalue – (float) The q-value threshold for identification

  • run – (str) The run name for which to get the identified proteins, if None, get for all runs

Returns:

The identified proteins across all runs (Dict[str, set]) or for a single run (set)

getNumIdentifiedPeptides(qvalue: float = 0.01, run: str | None = None) → int | Dict[str, int]

Get the number of identified peptides at a certain q-value.

Parameters:

  • qvalue – (float) The q-value threshold for identification

  • run – (str) The run name for which to get the identified peptides, if None, get for all runs

Returns:

The number of identified peptides across all runs (Dict[str, int]) or for a single run (int)

getNumIdentifiedPrecursors(qvalue: float = 0.01, run: str | None = None, precursorLevel=True) → int | Dict[str, int]

Get the number of identified precursors at a certain q-value.

Parameters:

  • qvalue – (float) The q-value threshold for identification

  • run – (str) The run name for which to get the identified precursors, if None, get for all runs

  • precursorLevel – (bool) If True, only check precursors qvalue, else check qvalue at precursor/peptide/protein level

getNumIdentifiedProteins(qvalue: float = 0.01, run: str | None = None) → int | Dict[str, int]

Get the number of identified proteins at a certain q-value.

Parameters:

  • qvalue – (float) The q-value threshold for identification

  • run – (str) The run name for which to get the identified proteins, if None, get for all runs

Returns:

The number of identified proteins across all runs (Dict[str, int]) or for a single run (int)

getPrecursorCVs(**kwargs) → DataFrame

Returns a DataFrame with the coefficient of variation for each precursor.

Parameters:

**kwargs (dict) – Additional arguments to be passed to the getIdentifiedPrecursors function