imspy.algorithm.intensity package

Submodules

imspy.algorithm.intensity.predictors module

class imspy.algorithm.intensity.predictors.IonIntensityPredictor

Bases: ABC

ABSTRACT INTERFACE for simulation of ion-mobility apex value

abstractmethod simulate_ion_intensities(sequences, charges, collision_energies)

Return type:

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

abstractmethod simulate_ion_intensities_pandas(data)

Return type:

DataFrame

class imspy.algorithm.intensity.predictors.Prosit2023TimsTofWrapper(verbose=True, model_name='deep_ion_intensity_predictor')

Bases: IonIntensityPredictor

Wrapper for the Prosit 2023 TIMS-TOF predictor

predict_intensities(sequences, charges, collision_energies, divide_collision_energy_by=100.0, batch_size=512, flatten=False)

Return type:

List[ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]]

simulate_ion_intensities(sequences, charges, collision_energies, divide_collision_energy_by=100.0, batch_size=512)

Return type:

List[PeptideProductIonSeriesCollection]

simulate_ion_intensities_pandas(data, batch_size=512, divide_collision_energy_by=100.0, verbose=False, flatten=False)

Return type:

DataFrame

simulate_ion_intensities_pandas_batched(data, batch_size_tf_ds=1024, batch_size=400000, divide_collision_energy_by=100.0)

Return type:

DataFrame

imspy.algorithm.intensity.predictors.get_collision_energy_calibration_factor(sample, model, lower=-30, upper=30, verbose=False)

Get the collision energy calibration factor for a given sample. :type sample: List[Psm] :param sample: a list of PeptideSpectrumMatch objects :type model: Prosit2023TimsTofWrapper :param model: a Prosit2023TimsTofWrapper object :type lower: int :param lower: lower bound for the search :type upper: int :param upper: upper bound for the search :type verbose: bool :param verbose: whether to print progress

Returns:

the collision energy calibration factor and the angle similarities

Return type:

Tuple[float, List[float]]

imspy.algorithm.intensity.predictors.load_prosit_2023_timsTOF_predictor()

Get a pretrained deep predictor model This model was downloaded from ZENODO: https://zenodo.org/records/8211811 PAPER : https://doi.org/10.1101/2023.07.17.549401 :returns: The pretrained deep predictor model

imspy.algorithm.intensity.predictors.predict_intensities_prosit(psm_collection, calibrate_collision_energy=True, verbose=False, num_threads=-1)

Predict the fragment ion intensities using Prosit. :type psm_collection: List[Psm] :param psm_collection: a list of peptide-spectrum matches :type calibrate_collision_energy: bool :param calibrate_collision_energy: whether to calibrate the collision energy :type verbose: bool :param verbose: whether to print progress :type num_threads: int :param num_threads: number of threads to use

Return type:

None

Returns:

None, the fragment ion intensities are stored in the PeptideSpectrumMatch objects

imspy.algorithm.intensity.predictors.remove_unimod_annotation(sequence)

Remove the unimod annotation from a peptide sequence. :type sequence: str :param sequence: a peptide sequence

Returns:

the peptide sequence without unimod annotation

Return type:

str

imspy.algorithm.intensity.utility module

imspy.algorithm.intensity.utility.beta_score(fragments_observed, fragments_predicted)

The beta score is a variant of the OpenMS proposed score calculation, using predicted intensities instead of a constant value for the expected intensity. :type fragments_observed: :param fragments_observed: The Sage Fragment object containing the observed intensities :type fragments_predicted: :param fragments_predicted: The Sage Fragment object containing the predicted intensities, e.g. from Prosit

Returns:

The beta score, hyper score variant using predicted intensities instead of a constant value for the expected intensity

Return type:

float

imspy.algorithm.intensity.utility.generate_prosit_intensity_prediction_dataset(sequences, charges, collision_energies=None, remove_mods=False)

Generate a dataset for predicting fragment intensities using Prosit. :type sequences: List[str] :param sequences: A list of peptide sequences. :type charges: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]] :param charges: A numpy array of precursor charges. :type collision_energies: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]] | None :param collision_energies: A numpy array of collision energies. :type remove_mods: bool :param remove_mods: Whether to remove modifications from the sequences.

Returns:

A tf.data.Dataset object that yields batches of data in the format expected by the model.

imspy.algorithm.intensity.utility.get_prosit_intensity_flat_labels()

Get the list of fragment ion labels for Prosit. :rtype: List[str] :returns: List of fragment ion labels, giving the returned order of fragment intensities.

imspy.algorithm.intensity.utility.post_process_predicted_fragment_spectra(data_pred)

post process the predicted fragment intensities :type data_pred: DataFrame :param data_pred: dataframe containing the predicted fragment intensities

Return type:

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

Returns:

numpy array of fragment intensities

imspy.algorithm.intensity.utility.seq_to_index(seq, max_length=30)

Convert a sequence to a list of indices into the alphabet.

Parameters:

• seq (str) – A string representing a sequence of amino acids.

• max_length (int) – The maximum length of the sequence to allow.

Return type:

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

Returns:

A list of integers, each representing an index into the alphabet.

imspy.algorithm.intensity.utility.to_prosit_tensor(sequences)

translate a list of fixed length numpy arrays into a tensorflow tensor :type sequences: List :param sequences: list of numpy arrays, representing peptide sequences

Return type:

Tensor

Returns:

tensorflow tensor

imspy.algorithm.intensity.utility.unpack_dict(features)

Unpack the dictionary of features into the expected inputs for the model.

Parameters:

features – A dictionary of features, with keys ‘peptides_in’, ‘precursor_charge_in’, and ‘collision_energy_in’

Returns:

(peptides, precursor_charge, collision_energy)

Return type:

A tuple of the expected inputs for the model

Module contents