skripts.FIA package

Submodules

skripts.FIA.FIA module

Methods for Flow-injection-analysis.

skripts.FIA.FIA.accurate_mass_search(consensus_map: _ConsensusMapDF, database_dir: str, tmp_dir: str, positive_adducts_file: str, negative_adducts_file: str, HMDBMapping_file: str, HMDB2StructMapping_file: str, ionization_mode: str = 'auto') → DataFrame

Assigning metbolites to consensus map masses.

Parameters:

• consensus_map (pyopenms.ConsensusMap) – Input consensus map

• database_dir (str) – Database directory

• tmp_dir (str) – Directory for temporary saves

• positive_adducts_file (str) – File with possible positive adducts

• negative_adducts_file (str) – File with possible negative adducts

• HMDBMapping_file (str) – HMDBMapping file

• HMDB2StructMapping_file (str) – HMDB2StructMapping file

• ionization_mode (str, optional) – Ionization mode, defaults to “auto”

Returns:

Annotated dataframe

Return type:

pandas.DataFrame

skripts.FIA.FIA.align_retention_times(feature_maps: list, max_num_peaks_considered: int = -1, max_mz_difference: float = 10.0, mz_unit: str = 'ppm', superimposer_max_scaling: float = 2.0) → list

Use as reference for alignment, the file with the largest number of features Works well if you have a pooled QC for example. Returns the aligned map at the first position.

Parameters:

• feature_maps (list) – List of feature maps

• max_num_peaks_considered (int, optional) – Maximum number of considered peaks, defaults to -1

• max_mz_difference (float, optional) – Maximum m/z difference, defaults to 10.0

• mz_unit (str, optional) – Unit for m/z values, defaults to “ppm”

• superimposer_max_scaling (float, optional) – Maximum scaling during superimposition, defaults to 2.0

Returns:

List of feature maps with aligned retention times

Return type:

list

skripts.FIA.FIA.annotate_consensus_map_df(consensus_map_df: DataFrame, mass_search_df: DataFrame, result_path: str = '.', mz_tolerance: float = 1e-05) → DataFrame

Annotate consensus map DataFrame.

Parameters:

• consensus_map_df (pd.DataFrame) – Input Consensus map DataFrame

• mass_search_df (pd.DataFrame) – Mass search DataFrame

• result_path (str, optional) – Path to output results, defaults to “.”

• mz_tolerance (float, optional) – Tolerance of m/z deviation, defaults to 1e-05

Returns:

Identified Metabolites DataFrame

Return type:

pd.DataFrame

skripts.FIA.FIA.assign_feature_maps_polarity(feature_maps: list, scan_polarity: str | None = None) → list

Assigns the polarity to a list of feature maps, depending on “pos”/”neg” in file name.

Parameters:

• feature_maps (list) – List of feature maps

• scan_polarity (Optional[str], optional) – Scan polarity, defaults to None

Returns:

List of feature maps with annotated polarity

Return type:

list

skripts.FIA.FIA.batch_download(base_url: str, file_urls: list, save_directory: str) → None

Download files from a list into a directory.

Parameters:

• base_url (str) – Base URL

• file_urls (list) – Individual file URLS which are appended to the base

• save_directory (str) – Directory to save files to.

skripts.FIA.FIA.bin_df_stepwise(df: ~pandas.core.frame.DataFrame | ~polars.dataframe.frame.DataFrame, binning_var='mz', binned_var='inty', statistic='sum', start: float = 0.0, stop: float = 2000.0, step: float = 0.001, backend=<module 'pandas' from '/mnt/lustre/groups/link/linca945/.conda/envs/VAE/lib/python3.11/site-packages/pandas/__init__.py'>) → DataFrame | DataFrame

Stepwise binning of a dataframe into discrete boxes.

Parameters:

• df (Union[pd.DataFrame, pl.DataFrame]) – Input dataframe

• binning_var (str, optional) – Binning variable, i.e. distance defining value (column in dataframe), defaults to “mz”

• binned_var (str, optional) – Binned value, i.e. combined value (column in dataframe), defaults to “inty”

• statistic – Operation to perform on binned values in a window. May take common parameters,

defined by sci.stats.binned_statistic. defaults to “sum” :type statistic: str, optional :param start: Starting binning variable point, defaults to 0.0 :type start: float, optional :param stop: Stopping binning variable point, defaults to 2000.0 :type stop: float, optional :param step: Step distance along binning variable, defaults to 0.001 :type step: float, optional :param backend: Backend (pandas or polars), defaults to pd :type backend: _type_, optional :return: Binned dataframe :rtype: Union[pd.DataFrame, pl.DataFrame]

skripts.FIA.FIA.bin_df_stepwise_batch(experiments: ~pandas.core.frame.DataFrame | ~polars.dataframe.frame.DataFrame, sample_var: str = 'sample', experiment_var: str = 'experiment', binning_var='mz', binned_var='inty', statistic='sum', start: float = 0.0, stop: float = 2000.0, step: float = 0.001, backend=<module 'pandas' from '/mnt/lustre/groups/link/linca945/.conda/envs/VAE/lib/python3.11/site-packages/pandas/__init__.py'>) → DataFrame | DataFrame

Stepwise binning of a dataframe into discrete boxes of multiple dataframes.

Parameters:

• experiments (Union[pd.DataFrame, pl.DataFrame]) – Input experiments

• binning_var (str, optional) – Binning variable, i.e. distance defining value (column in dataframe), defaults to “mz”

• binned_var (str, optional) – Binned value, i.e. combined value (column in dataframe), defaults to “inty”

• statistic – Operation to perform on binned values in a window. May take common parameters,

defined by sci.stats.binned_statistic. defaults to “sum” :type statistic: str, optional :param start: Starting binning variable point, defaults to 0.0 :type start: float, optional :param stop: Stopping binning variable point, defaults to 2000.0 :type stop: float, optional :param step: Step distance along binning variable, defaults to 0.001 :type step: float, optional :param backend: Backend (pandas or polars), defaults to pd :type backend: _type_, optional :return: Binned dataframe :rtype: Union[pd.DataFrame, pl.DataFrame]

skripts.FIA.FIA.bits_to_bytes(bits: float | int, factor: float | int) → float

Coverts a number of bits to a number of bytes for readability.

Parameters:

• bits (Union[float, int]) – Number of bits to be converted

• factor (Union[float, int]) – / 10**factor (e.g. use 9 for GB)

Returns:

Number of bytes

Return type:

float

skripts.FIA.FIA.build_directory(dir_path: str) → None

Build a new directory in the given path.

Parameters:

dir_path (str) – Directory path

skripts.FIA.FIA.centroid_batch(experiments: Sequence[_MSExperimentDF | str] | str, run_dir: str, file_ending: str = '.mzML', instrument: str = 'TOF', signal_to_noise: float = 1.0, spacing_difference_gap: float = 4.0, spacing_difference: float = 1.5, missing: int = 1, ms_levels: List[int] = [], report_FWHM: str = 'true', report_FWHM_unit: str = 'relative', max_intensity: float = -1, auto_max_stdev_factor: float = 3.0, auto_max_percentile: int = 95, auto_mode: int = 0, win_len: float = 200.0, bin_count: int = 30, min_required_elements: int = 10, noise_for_empty_window: float = 1e+20, write_log_messages: str = 'true', peak_width: float = 0.0, sn_bin_count: int = 30, nr_iterations: int = 5, sn_win_len: float = 20.0, check_width_internally: str = 'false', ms1_only: str = 'true', clear_meta_data: str = 'false', deepcopy: bool = False) → str

Centroids a batch of experiments, extracted from files in a given directory with a given file ending (i.e. .mzML or .mzXML). Returns the new directors as path/centroids.

Parameters:

• experiments (Union[Sequence[Union[oms.MSExperiment,str]], str]) – Input experiments

• run_dir (str) – Run directory

• file_ending (str, optional) – File ending, defaults to “.mzML”

• instrument (str, optional) – Instrument type (TOF or FT-ICR, Orbitrap), defaults to “TOF”

• signal_to_noise (float, optional) – Signal to noise ratio, defaults to 1.0

• spacing_difference_gap (float, optional) – Spacing difference gap, defaults to 4.0

• spacing_difference (float, optional) – Spacing difference, defaults to 1.5

• missing (int, optional) – Number of allowed missing values, defaults to 1

• ms_levels (List[int], optional) – MS levels to consider, defaults to []

• report_FWHM (str, optional) – Report full width at half maximum, defaults to “true”

• report_FWHM_unit (str, optional) – Report full width at half maximum unit, defaults to “relative”

• max_intensity (float, optional) – Maximum intensity, defaults to -1

• auto_max_stdev_factor (float, optional) – Automatic maximal standard deviation factor, defaults to 3.0

• auto_max_percentile (int, optional) – Automatic maximal percentile to consider, defaults to 95

• auto_mode (int, optional) – Automatic mode (0/1), defaults to 0

• win_len (float, optional) – Window length, defaults to 200.0

• bin_count (int, optional) – Number of bins, defaults to 30

• min_required_elements (int, optional) – Minimum required elements for a peak, defaults to 10

• noise_for_empty_window (float, optional) – Noise value for an empty window, defaults to 1e+20

• write_log_messages (str, optional) – Write log messages (true/false), defaults to “true”

• peak_width (float, optional) – Expected peak width, defaults to 0.0

• sn_bin_count (int, optional) – Signal bin count, defaults to 30

• nr_iterations (int, optional) – Iterations to recenter peaks, defaults to 5

• sn_win_len (float, optional) – Signal window length, defaults to 20.0

• check_width_internally (str, optional) – Check width internally, defaults to “false”

• ms1_only (str, optional) – Only MS1 spectrum, defaults to “true”

• clear_meta_data (str, optional) – Clear meta data, defaults to “false”

• deepcopy (bool, optional) – Perform a deepcopy to relieably unlink the experiment from the input, defaults to False

Returns:

Directory with centroids

Return type:

str

skripts.FIA.FIA.centroid_experiment(experiment: _MSExperimentDF | str, instrument: str = 'TOF', signal_to_noise: float = 1.0, spacing_difference_gap: float = 4.0, spacing_difference: float = 1.5, missing: int = 1, ms_levels: List[int] = [], report_FWHM: str = 'true', report_FWHM_unit: str = 'relative', max_intensity: float = -1, auto_max_stdev_factor: float = 3.0, auto_max_percentile: int = 95, auto_mode: int = 0, win_len: float = 200.0, bin_count: int = 30, min_required_elements: int = 10, noise_for_empty_window: float = 1e+20, write_log_messages: str = 'true', peak_width: float = 0.0, sn_bin_count: int = 30, nr_iterations: int = 5, sn_win_len: float = 20.0, check_width_internally: str = 'false', ms1_only: str = 'true', clear_meta_data: str = 'false', deepcopy: bool = False) → _MSExperimentDF

Reduce dataset to centroids.

Usecase fia_df[“cent_experiment”] = fia_df[“experiment”].apply(lambda experiment: centroid_experiment(experiment, instrument=”TOF”, # For All

signal_to_noise=2.0, spacing_difference=1.5,

spacing_difference_gap=4.0, missing=1, ms_levels=[1], # For Orbitrap report_FWHM=”true”, report_FWHM_unit=”relative”, max_intensity=-1, auto_max_stdev_factor=3.0, auto_max_percentile=95, auto_mode=0, win_len=200.0, bin_count=30, min_required_elements=10, noise_for_empty_window=1e+20, write_log_messages=”true”,

peak_width=0.0, sn_bin_count=30, nr_iterations=5, sn_win_len=20.0, # For TOF check_width_internally=”false”, ms1_only=”true”, clear_meta_data=”false”, deepcopy=False))

Parameters:

• experiment (Union[pyopenms.MSExperiment, str]) – Input experiment

• instrument (str, optional) – Instrument type (TOF or FT-ICR, Orbitrap), defaults to “TOF”

• signal_to_noise (float, optional) – Signal to noise ratio, defaults to 1.0

• spacing_difference_gap (float, optional) – Spacing difference gap, defaults to 4.0

• spacing_difference (float, optional) – Spacing difference, defaults to 1.5

• missing (int, optional) – Number of allowed missing values, defaults to 1

• ms_levels (List[int], optional) – MS levels to consider, defaults to []

• report_FWHM (str, optional) – Report full width at half maximum, defaults to “true”

• report_FWHM_unit (str, optional) – Report full width at half maximum unit, defaults to “relative”

• max_intensity (float, optional) – Maximum intensity, defaults to -1

• auto_max_stdev_factor (float, optional) – Automatic maximal standard deviation factor, defaults to 3.0

• auto_max_percentile (int, optional) – Automatic maximal percentile to consider, defaults to 95

• auto_mode (int, optional) – Automatic mode (0/1), defaults to 0

• win_len (float, optional) – Window length, defaults to 200.0

• bin_count (int, optional) – Number of bins, defaults to 30

• min_required_elements (int, optional) – Minimum required elements for a peak, defaults to 10

• noise_for_empty_window (float, optional) – Noise value for an empty window, defaults to 1e+20

• write_log_messages (str, optional) – Write log messages (true/false), defaults to “true”

• peak_width (float, optional) – Expected peak width, defaults to 0.0

• sn_bin_count (int, optional) – Signal bin count, defaults to 30

• nr_iterations (int, optional) – Iterations to recenter peaks, defaults to 5

• sn_win_len (float, optional) – Signal window length, defaults to 20.0

• check_width_internally (str, optional) – Check width internally, defaults to “false”

• ms1_only (str, optional) – Only MS1 spectrum, defaults to “true”

• clear_meta_data (str, optional) – Clear meta data, defaults to “false”

• deepcopy (bool, optional) – Perform a deepcopy to relieably unlink the experiment from the input, defaults to False

Returns:

Centroided experiment

Return type:

pyopenms.MSExperiment

skripts.FIA.FIA.check_ending_experiment(file: str) → bool

Check whether the file has a mzML or mzXML ending.

Parameters:

file (str) – Path to file

Returns:

Ending is mzML or mzXML

Return type:

bool

skripts.FIA.FIA.clean_dir(dir_path: str, subfolder: str | None = None) → str

Delete a directory or its subfolder and reconstruct the directory.

Parameters:

• dir_path (str) – Directory path

• subfolder (Optional[str], optional) – Subfolder path, defaults to None

Returns:

Directory path

Return type:

str

skripts.FIA.FIA.cluster_matlab(df: DataFrame, height_lim: int = 1000, prominence_lim: int = 1000, threshold: float = 0.004900000000000001)

Clusters according to FIA matlab routine

Parameters:

• df (pd.DataFrame) – Input dataframe

• height_lim (int, optional) – height limit, defaults to 1000

• prominence_lim (int, optional) – prominence limit, defaults to 1000

• threshold (float, optional) – threshold to cut off values, defaults to (7e-2)**2

Returns:

m/z + inty as paired array

Return type:

np.ndarray

skripts.FIA.FIA.cluster_sliding_window(comb_experiment: _MSExperimentDF, height_lim: int = 1000, prominence_lim: int = 1000, window_len: int = 2000, window_shift=1000, threshold: float = 0.004900000000000001)

Applies clustering over sliding window in an experiment. The result may contain duplicates or close to duplicates.

Parameters:

comb_experiment (pyopenms.MSExperiment) – Input experiment

param height_lim: height limit, defaults to 1000 :type height_lim: int, optional :param prominence_lim: prominence limit, defaults to 1000 :type prominence_lim: int, optional :param window_len: Window length, defaults to 2000 :type window_len: int, optional :param window_shift: Window shift, defaults to 1000 :type window_shift: int, optional :param threshold: threshold to cut off values, defaults to (7e-2)**2 :type threshold: float, optional :return: Clustered experiment :rtype: pyopenms.MSExperiment

skripts.FIA.FIA.combine_spectra_experiments(spectra_container: Sequence[_MSExperimentDF | MSSpectrum]) → _MSExperimentDF

Combines all spectra/experiements, into different spectra in one experiment

Parameters:

spectra_container (Sequence[Union[oms.MSExperiment,oms.MSSpectrum]]) – Input experiments

Returns:

Experiment with summed intensities along m/z axis

Return type:

oms.MSExperiment

skripts.FIA.FIA.consensus_features_linking(feature_maps: list, feature_grouper_type: str = 'QT') → _ConsensusMapDF

Linking features by consensus voting.

Parameters:

• feature_maps (list) – List of feature maps

• feature_grouper_type (str, optional) – Quality threshold clustering (QT) or k-dimensional tree clustering, defaults to “QT”

Raises:

ValueError – Use QT or KD for feature groupers.

Returns:

Consensums map of features

Return type:

pyopenms.ConsensusMap

skripts.FIA.FIA.consensus_map_to_df(consensus_map: _ConsensusMapDF) → DataFrame

Transforms a consensus map into a daraframe

Parameters:

consensus_map (pyopenms.ConsensusMap) – Input consensus map

Returns:

Dataframe from consensus map

Return type:

pandas.DataFrame

skripts.FIA.FIA.copy_experiment(experiment: _MSExperimentDF) → _MSExperimentDF

Makes a complete (recursive) copy of an experiment.

Parameters:

experiment (pyopenms.MSExperiment) – Experiment

Returns:

Copy of experiment.

Return type:

pyopenms.MSExperiment

skripts.FIA.FIA.define_metabolite_table(path_to_library_file: str, mass_range: list) → list

Read tsv file and create list of FeatureFinderMetaboIdentCompound.

Parameters:

• path_to_library_file (str) – Path to library file

• mass_range (list) – Range of m/z values

Returns:

Metabolite table

Return type:

list

skripts.FIA.FIA.deisotope_experiment(experiment: _MSExperimentDF | str, fragment_tolerance: float = 0.1, fragment_unit_ppm: bool = False, min_charge: int = 1, max_charge: int = 3, keep_only_deisotoped: bool = True, min_isopeaks: int = 2, max_isopeaks: int = 10, make_single_charged: bool = True, annotate_charge: bool = True, annotate_iso_peak_count: bool = True, use_decreasing_model: bool = True, start_intensity_check: bool = False, add_up_intensity: bool = False, deepcopy: bool = False)

Attempt to combine isotopes in an experiment through exhaustive calculation.

Parameters:

• spectrum (Union[oms.MSExperiment, str]) – Input experiment

• fragment_tolerance (float, optional) – Tolerance for fragments, defaults to 0.1

• fragment_unit_ppm (bool, optional) – Use ppm as fragmentation unit, defaults to False

• min_charge (int, optional) – Minimal charge, defaults to 1

• max_charge (int, optional) – Maximum charge, defaults to 3

• keep_only_deisotoped (bool, optional) – Keep only deisotoped signals, defaults to True

• min_isopeaks (int, optional) – Minimum amount of isotopes for a signal, defaults to 2

• max_isopeaks (int, optional) – Maximum amount of isotopes in a signal, defaults to 10

• make_single_charged (bool, optional) – Adapt isotops to single hydrogen adducts/deducts, defaults to True

• annotate_charge (bool, optional) – Annotate the charge, defaults to True

• annotate_iso_peak_count (bool, optional) – Annotate isotopic peak count, defaults to True

• use_decreasing_model (bool, optional) – Use decreasing model (decreased chance of isotopes with further changes), defaults to True

• start_intensity_check (bool, optional) – Intensity check at the start, defaults to False

• add_up_intensity (bool, optional) – Add intensity of isotopes, defaults to False

Returns:

Deisotoped experiment

Return type:

oms.MSExperiment

skripts.FIA.FIA.deisotope_spectrum(spectrum: MSSpectrum, fragment_tolerance: float = 0.1, fragment_unit_ppm: bool = False, min_charge: int = 1, max_charge: int = 3, keep_only_deisotoped: bool = True, min_isopeaks: int = 2, max_isopeaks: int = 10, make_single_charged: bool = True, annotate_charge: bool = True, annotate_iso_peak_count: bool = True, use_decreasing_model: bool = True, start_intensity_check: bool = False, add_up_intensity: bool = False) → MSSpectrum

Attempt to combine isotopes in a spectrum through exhaustive calculation.

Parameters:

• spectrum (pyopenms.MSSpectrum) – Input spectrum

• fragment_tolerance (float, optional) – Tolerance for fragments, defaults to 0.1

• fragment_unit_ppm (bool, optional) – Use ppm as fragmentation unit, defaults to False

• min_charge (int, optional) – Minimal charge, defaults to 1

• max_charge (int, optional) – Maximum charge, defaults to 3

• keep_only_deisotoped (bool, optional) – Keep only deisotoped signals, defaults to True

• min_isopeaks (int, optional) – Minimum amount of isotopes for a signal, defaults to 2

• max_isopeaks (int, optional) – Maximum amount of isotopes in a signal, defaults to 10

• make_single_charged (bool, optional) – Adapt isotops to single hydrogen adducts/deducts, defaults to True

• annotate_charge (bool, optional) – Annotate the charge, defaults to True

• annotate_iso_peak_count (bool, optional) – Annotate isotopic peak count, defaults to True

• use_decreasing_model (bool, optional) – Use decreasing model (decreased chance of isotopes with further changes), defaults to True

• start_intensity_check (bool, optional) – Intensity check at the start, defaults to False

• add_up_intensity (bool, optional) – Add intensity of isotopes, defaults to False

Returns:

Deisotoped spectrum

Return type:

pyopenms.MSSpectrum

skripts.FIA.FIA.detect_adducts(feature_maps: list, potential_adducts: str | bytes = '[]', q_try: str = 'feature', mass_max_diff: float = 10.0, unit: str = 'ppm', max_minority_bound: int = 3, verbose_level: int = 0) → list

Attempt adduct detection through exhaustive calculations.

Parameters:

• feature_maps (list) – List of feature maps

• potential_adducts (Union[str, bytes], optional) – Potential adducts to consider, defaults to “[]”

• q_try (str, optional) – Charge discovery dimension, defaults to “feature”

• mass_max_diff (float, optional) – Maximum mass difference, defaults to 10.0

• unit (str, optional) – Unit of mass difference, defaults to “ppm”

• max_minority_bound (int, optional) – Maximum minority bound, defaults to 3

• verbose_level (int, optional) – Verbosity level, defaults to 0

Returns:

Feature maps with removed adducts (deconvoluted)

Return type:

list

skripts.FIA.FIA.dynamic_plot(experiment: _MSExperimentDF, mode: str = 'lines', log: List[str] = ['x']) → None

Shows an interactive plot of all spectra in the experiment. May take a long time for large datasets. Recommended after centroiding, or data reduction.

Parameters:

• experiment (pyopenms.MSExperiment) – Input experiment

• mode (str, optional) – Mode of display [“lines” | “markers” | “lines+markers” | other pyplot.graph_objects options], defaults to “lines”

• log (List[str], optional) – Axes to log-transform [x,y], defaults to []

skripts.FIA.FIA.elution_peak_detection(mass_traces: list, chrom_fwhm: float = 10.0, chrom_peak_snr: float = 2.0, width_filtering: str = 'fixed', min_fwhm: float = 1.0, max_fwhm: float = 60.0, masstrace_snr_filtering: str = 'false') → list

Elution peak detection along mass traces. Relevant for chromatographic data.

Parameters:

• mass_traces (list) – List of mass traces

• chrom_fwhm (float, optional) – Chromatographic full width at half maximum, defaults to 10.0

• chrom_peak_snr (float, optional) – Minimum signal-to-noise a mass trace should have, defaults to 2.0

• width_filtering (str, optional) – Type of width filtering, defaults to “fixed”

• min_fwhm (float, optional) – Minimal full width at half maximum, defaults to 1.0

• max_fwhm (float, optional) – Maximal full width at half maximum, defaults to 60.0

• masstrace_snr_filtering (str, optional) – Filtering by signal to noise ratio, defaults to “false”

Returns:

List of final mass traces

Return type:

list

skripts.FIA.FIA.elution_peak_detection_batch(mass_traces_all: list[list], chrom_fwhm: float = 10.0, chrom_peak_snr: float = 2.0, width_filtering: str = 'fixed', min_fwhm: float = 1.0, max_fwhm: float = 60.0, masstrace_snr_filtering: str = 'false') → list[list]

Elution peak detection along list of mass traces. Relevant for chromatographic data.

Parameters:

• mass_traces_all (list[list]) – List of list of all mass traces

• chrom_fwhm (float, optional) – Chromatographic full width at half maximum, defaults to 10.0

• chrom_peak_snr (float, optional) – Minimum signal-to-noise a mass trace should have, defaults to 2.0

• width_filtering (str, optional) – Type of width filtering, defaults to “fixed”

• min_fwhm (float, optional) – Minimal full width at half maximum, defaults to 1.0

• max_fwhm (float, optional) – Maximal full width at half maximum, defaults to 60.0

• masstrace_snr_filtering (str, optional) – Filtering by signal to noise ratio, defaults to “false”

Returns:

List of list of final mass traces

Return type:

list[list]

skripts.FIA.FIA.extract_feature_coord(feature: Feature, mzs: ndarray, retention_times: ndarray, intensities: ndarray, labels: ndarray, sub_feat: Feature | None = None) → list

Extract feature coordinates for plots

Parameters:

• feature (oms.Feature) – Input feature

• mzs (np.ndarray) – m/z values

• retention_times (np.ndarray) – Retention times

• intensities (np.ndarray) – Intensity values

• labels (np.ndarray) – Labels

• sub_feat (Optional[oms.Feature], optional) – Sub-features, defaults to None

Returns:

List of mzs, retention times intensities and matching labels for plots

Return type:

list

skripts.FIA.FIA.extract_from_clustering(df: DataFrame, clustering) → ndarray

Extract mzs and intensities from clustering

Parameters:

• df (pandas.DataFrame) – Input dataframe

• clustering (np.ndarray) – Clustering

Returns:

m/z + inty as paired array

Return type:

np.ndarray

skripts.FIA.FIA.feature_detection_targeted(experiment: _MSExperimentDF | str, metab_table: list, feature_filepath: str | None = None, mz_window: float = 5.0, rt_window: float | None = None, n_isotopes: int = 2, isotope_pmin: float = 0.01, peak_width: float = 60.0) → _FeatureMapDF

Feature detection with a given metabolic table.

Parameters:

• experiment (Union[oms.MSExperiment, str]) – Input experiment

• metab_table (list) – Metabilites table

• feature_filepath (Optional[str], optional) – Filpath to featureXML, defaults to None

• mz_window (float, optional) – m/z window width, defaults to 5.0

• rt_window (Optional[float], optional) – Retention time window width, defaults to None

• n_isotopes (int, optional) – Number of considered isotopes, defaults to 2

• isotope_pmin (float, optional) – Minimal probability of an isotope to be considered, defaults to 0.01

• peak_width (float, optional) – Standard peak width, defaults to 60.0

Returns:

Feature map

Return type:

oms.FeatureMap

skripts.FIA.FIA.feature_detection_untargeted(experiment: _MSExperimentDF | str, mass_traces_deconvol: list = [], isotope_filtering_model='metabolites (2% RMS)', local_rt_range: float = 3.0, local_mz_range: float = 5.0, charge_lower_bound: int = 1, charge_upper_bound: int = 3, chrom_fwhm: float = 10.0, report_summed_ints: str = 'true', enable_RT_filtering: str = 'false', mz_scoring_13C: str = 'false', use_smoothed_intensities: str = 'false', report_convex_hulls: str = 'true', report_chromatograms: str = 'false', remove_single_traces: str = 'true', mz_scoring_by_elements: str = 'false', elements: str = 'CHNOPS') → _FeatureMapDF

Untargeted feature detection in an experiment.

Parameters:

• experiment (Union[oms.MSExperiment, str]) – Input experiment

• mass_traces_deconvol (list, optional) – Deconvoluted mass traces, defaults to []

• isotope_filtering_model (str, optional) – Isotope filtering model, defaults to “metabolites (2% RMS)”

• local_rt_range (float, optional) – Local retention time range, defaults to 3.0

• local_mz_range (float, optional) – Local m/z range, defaults to 5.0

• charge_lower_bound (int, optional) – Lower charge bound, defaults to 1

• charge_upper_bound (int, optional) – Upper charge bound, defaults to 3

• chrom_fwhm (float, optional) – Chromatographic full width at half maximum, defaults to 10.0

• report_summed_ints (str, optional) – Report summed intensities, defaults to “true”

• enable_RT_filtering (str, optional) – Enable retention time filtering, defaults to “false”

• mz_scoring_13C (str, optional) – Score m/z by looking at expected Carbon13 peaks, defaults to “false”

• use_smoothed_intensities (str, optional) – Use smoothed intensities (if smoothed before), defaults to “false”

• report_convex_hulls (str, optional) – Report convex hulls, defaults to “true”

• report_chromatograms (str, optional) – Report chromatograms, defaults to “false”

• remove_single_traces (str, optional) – Remove single traces (only appear at one retention time), defaults to “true”

• mz_scoring_by_elements (str, optional) – Score m/z by present elements, defaults to “false”

• elements (str, optional) – Elements to consider, defaults to “CHNOPS”

Returns:

Feature Map

Return type:

pyopenms.FeatureMap

skripts.FIA.FIA.feature_detection_untargeted_batch(experiments: Sequence[_MSExperimentDF | str] | str, file_ending: str = '.mzML', mass_traces_deconvol_all: list[list] = [], isotope_filtering_model='metabolites (2% RMS)', local_rt_range: float = 3.0, local_mz_range: float = 5.0, charge_lower_bound: int = 1, charge_upper_bound: int = 3, chrom_fwhm: float = 10.0, report_summed_ints: str = 'true', enable_RT_filtering: str = 'false', mz_scoring_13C: str = 'false', use_smoothed_intensities: str = 'false', report_convex_hulls: str = 'true', report_chromatograms: str = 'false', remove_single_traces: str = 'true', mz_scoring_by_elements: str = 'false', elements: str = 'CHNOPS') → list[_FeatureMapDF]

Untargeted feature detection in experiments.

Parameters:

• experiments (Union[Sequence[Union[oms.MSExperiment,str]], str]) – Input experiments

• file_ending (str, optional) – File ending, defaults to “.mzML”

• mass_traces_deconvol (list, optional) – Deconvoluted mass traces, defaults to []

• isotope_filtering_model (str, optional) – Isotope filtering model, defaults to “metabolites (2% RMS)”

• local_rt_range (float, optional) – Local retention time range, defaults to 3.0

• local_mz_range (float, optional) – Local m/z range, defaults to 5.0

• charge_lower_bound (int, optional) – Lower charge bound, defaults to 1

• charge_upper_bound (int, optional) – Upper charge bound, defaults to 3

• chrom_fwhm (float, optional) – Chromatographic full width at half maximum, defaults to 10.0

• report_summed_ints (str, optional) – Report summed intensities, defaults to “true”

• enable_RT_filtering (str, optional) – Enable retention time filtering, defaults to “false”

• mz_scoring_13C (str, optional) – Score m/z by looking at expected Carbon13 peaks, defaults to “false”

• use_smoothed_intensities (str, optional) – Use smoothed intensities (if smoothed before), defaults to “false”

• report_convex_hulls (str, optional) – Report convex hulls, defaults to “true”

• report_chromatograms (str, optional) – Report chromatograms, defaults to “false”

• remove_single_traces (str, optional) – Remove single traces (only appear at one retention time), defaults to “true”

• mz_scoring_by_elements (str, optional) – Score m/z by present elements, defaults to “false”

• elements (str, optional) – Elements to consider, defaults to “CHNOPS”

Returns:

List of Feature Maps

Return type:

list[pyopenms.FeatureMap]

skripts.FIA.FIA.filter_consensus_map_df(consensus_map_df: DataFrame, max_missing_values: int = 1, min_feature_quality: float | None = 0.8) → DataFrame

Filter consensus map DataFrame according to missing values and feature quality.

Parameters:

• consensus_map_df (pandas.DataFrame) – Input consensus map dataframe

• max_missing_values (int, optional) – Maximum number of missing values, defaults to 1

• min_feature_quality (Optional[float], optional) – Minimal quality of feature, defaults to 0.8

Returns:

Filtered consensus map dataframe

Return type:

pandas.DataFrame

skripts.FIA.FIA.find_close(df1: DataFrame, df1_col, df2: DataFrame, df2_col, tolerance=0.001)

Find close values in two dataframes.

Parameters:

• df1 (pandas.DataFrame) – Input dataframe 1

• df1_col (all) – Input dataframe 1 matched column

• df2 (pandas.DataFrame) – Input dataframe 2

• df2_col (all) – Input dataframe 2 matched column

• tolerance (float, optional) – Absolute tolerance in difference, defaults to 0.001

Yield:

Datframe of close values

Return type:

pandas.DataFrame

skripts.FIA.FIA.impute_consensus_map_df(consensus_map_df: DataFrame, n_nearest_neighbours: int = 2) → DataFrame

Data imputation with k-neares-neighbours (kNN).

Parameters:

• consensus_map_df (pandas.DataFrame) – Input consensus map dataframe

• n_nearest_neighbours (int, optional) – K nearest neighbours, defaults to 2

Returns:

Consensus map dataframe with imputed values

Return type:

pandas.DataFrame

skripts.FIA.FIA.join_df_by(df: DataFrame, joiner: str, combiner: str) → DataFrame

Combines datframe with same <joiner>, while combining the name of <combiner> as the new index.

Parameters:

• df (pd.DataFrame) – Input DataFrame

• joiner (str) – Indicates the column that is the criterium for joining the rows

• combiner (str) – Indicates the column that should be combined as an identifier

Returns:

Combined DataFrame

Return type:

pd.DataFrame

skripts.FIA.FIA.limit_experiment(experiment: _MSExperimentDF | str, mz_lower_limit: int | float = 0, mz_upper_limit: int | float = 10000, sample_size: int = 100000, statistic: str = 'sum', deepcopy: bool = False) → _MSExperimentDF

Limits the range of all spectra in an experiment to <mz_lower_limit> and <mz_upper_limit>. Uniformly samples <sample_size> number of peaks from the spectrum (without replacement).

Parameters:

• experiment (Union[pyopenms.MSExperiment, str]) – Input experiment

• mz_lower_limit (Union[int, float], optional) – Lower m/z value limit, defaults to 0

• mz_upper_limit (Union[int, float], optional) – Upper m/z value limit, defaults to 10000

• sample_size (int, optional) – Number of sampled peaks from the spectrum, defaults to 100000

• statistic – Operation to perform on binned values in a window. May take common parameters,

defined by sci.stats.binned_statistic., defaults to “sum” :type statistic: str, optional :param deepcopy: Perform a deepcopy to relieably unlink the experiment from the input, defaults to False :type deepcopy: bool, optional :return: Limited and sampled experiment :rtype: oms.MSExperiment

skripts.FIA.FIA.limit_spectrum(spectrum: MSSpectrum, mz_lower_limit: int | float, mz_upper_limit: int | float, sample_size: int, statistic: str = 'sum') → MSSpectrum

Limits the range of the Spectrum to <mz_lower_limit> and <mz_upper_limit>. Uniformly samples <sample_size> number of peaks from the spectrum (without replacement).

Parameters:

• spectrum (pyopenms.MSSpectrum) – Input spectrum

• mz_lower_limit (Union[int, float]) – Lower m/z value limit

• mz_upper_limit (Union[int, float]) – Upper m/z value limit

• sample_size (int) – Number of sampled peaks from the spectrum

• statistic – Operation to perform on binned values in a window. May take common parameters,

defined by sci.stats.binned_statistic., defaults to “sum” :type statistic: str, optional :return: Limited and sampled spectrum :rtype: pyopenms.MSSpectrum

skripts.FIA.FIA.load_experiment(experiment: _MSExperimentDF | str, separator: str = '\t') → _MSExperimentDF

If no experiment is given, loads and returns it from either .mzML or .mzXML file. Collects garbage with gc.collect() to ensure space in the RAM.

Parameters:

• experiment (Union[oms.MSExperiment, str]) – Experiment, or Path to experiment

• separator (str, optional) – Separator of data, defaults to ” “

Returns:

Experiment

Return type:

pyopenms.MSExperiment

skripts.FIA.FIA.load_experiments(experiments: Sequence[_MSExperimentDF | str] | str, file_ending: str | None = None, separator: str = '\t', data_load: bool = True) → Sequence[_MSExperimentDF | str]

Load a batch of experiments.

Parameters:

experiments – Experiments, either described by a list of paths or one path as base directory,

or an existing experiment. :type experiments: Union[Sequence[Union[oms.MSExperiment,str]], str] :param file_ending: Ending of experiment file, defaults to None :type file_ending: Optional[str], optional :param separator: Separator of data, defaults to ” ” :type separator: str, optional :param data_load: Load the data or just combine the base string to a list of full filepaths, defaults to True :type data_load: bool, optional :return: Experiments :rtype: Sequence[Union[oms.MSExperiment,str]]

skripts.FIA.FIA.load_fia_df(data_dir: str, file_ending: str, separator: str = '\t', data_load: bool = True, backend=<module 'pandas' from '/mnt/lustre/groups/link/linca945/.conda/envs/VAE/lib/python3.11/site-packages/pandas/__init__.py'>) → DataFrame | DataFrame

Load a Flow injection analysis dataframe, defining important properties.

Parameters:

• data_dir (str) – Data directory

• file_ending (str) – Ending of file

• separator (str, optional) – Separator for file, defaults to ” “

• data_load (bool, optional) – Load data or only return list of experiments, defaults to True

• backend (_type_, optional) – Use pandas or polars as backend, defaults to pd

Returns:

_description_

Return type:

Union[pandas.DataFrame, polars.DataFrame]

skripts.FIA.FIA.load_name(experiment: _MSExperimentDF | str, alt_name: str | None = None, file_ending: str | None = None) → str

Load the name of an experiment.

Parameters:

• experiment (Union[oms.MSExperiment, str]) – Experiment

• alt_name (Optional[str], optional) – Alternative Name if none is found, defaults to None

• file_ending (Optional[str], optional) – Ending of experiment file, defaults to None

Raises:

ValueError – Raises error if no file name is found and no alt_name is given.

Returns:

Name of experiment or alternative name

Return type:

str

skripts.FIA.FIA.load_names_batch(experiments: Sequence[_MSExperimentDF | str] | str, file_ending: str = '.mzML') → List[str]

If no experiment is given, loads and returns it from either .mzML or .mzXML file.

Parameters:

• experiments (Union[Sequence[Union[oms.MSExperiment,str]], str]) – Experiments

• file_ending (str, optional) – Ending of experiment file, defaults to “.mzML”

Returns:

List of experiment names

Return type:

List[str]

skripts.FIA.FIA.make_merge_dict(dir: str, file_ending: str = '.mzML') → dict

Create a dictionary for merging.

Parameters:

• dir (str) – Directory to extract samples from.

• file_ending (str, optional) – File ending, defaults to “.mzML”

Returns:

Dictonary with sample names as keys and paths for merging as values

Return type:

dict

skripts.FIA.FIA.mass_trace_detection(experiment: _MSExperimentDF | str, mass_error_ppm: float = 10.0, noise_threshold_int: float = 1000.0, reestimate_mt_sd: str = 'true', quant_method: str = 'median', trace_termination_criterion: str = 'outlier', trace_termination_outliers: int = 3, min_trace_length: float = 5.0, max_trace_length: float = -1.0) → list

Detection of mass traces in experiment over several spectra.

Parameters:

• experiment (Union[oms.MSExperiment, str]) – Input experiment

• mass_error_ppm (float, optional) – Mass error in ppm, defaults to 10.0

• noise_threshold_int (float, optional) – Noise threshold intensity, defaults to 1000.0

• reestimate_mt_sd (str, optional) – Reestimate mass trace standard deviation during run, defaults to “true”

• quant_method (str, optional) – Quantification method, defaults to “median”

• trace_termination_criterion (str, optional) – Criterion to terminate a trace, defaults to “outlier”

• trace_termination_outliers (int, optional) – Number of cases that fulfil criterion/outliers to break trace, defaults to 3

• min_trace_length (float, optional) – Minimal trace length, defaults to 5.0

• max_trace_length (float, optional) – Maximum trace length, defaults to -1.0

Returns:

Mass traces

Return type:

list

skripts.FIA.FIA.mass_trace_detection_batch(experiments: Sequence[_MSExperimentDF | str] | str, file_ending: str = '.mzML', mass_error_ppm: float = 10.0, noise_threshold_int: float = 1000.0, reestimate_mt_sd: str = 'true', quant_method: str = 'median', trace_termination_criterion: str = 'outlier', trace_termination_outliers: int = 3, min_trace_length: float = 5.0, max_trace_length: float = -1.0) → list

Detection of mass traces in experiment over several experiments.

Parameters:

• experiments (Union[Sequence[Union[oms.MSExperiment,str]], str]) – Input experiments

• file_ending (str, optional) – File ending, defaults to “.mzML”

• mass_error_ppm (float, optional) – Mass error in ppm, defaults to 10.0

• noise_threshold_int (float, optional) – Noise threshold intensity, defaults to 1000.0

• reestimate_mt_sd (str, optional) – Reestimate mass trace standard deviation during run, defaults to “true”

• quant_method (str, optional) – Quantification method, defaults to “median”

• trace_termination_criterion (str, optional) – Criterion to terminate a trace, defaults to “outlier”

• trace_termination_outliers (int, optional) – Number of cases that fulfil criterion/outliers to break trace, defaults to 3

• min_trace_length (float, optional) – Minimal trace length, defaults to 5.0

• max_trace_length (float, optional) – Maximum trace length, defaults to -1.0

Returns:

Mass traces

Return type:

list

skripts.FIA.FIA.merge_batch(experiments: Sequence[_MSExperimentDF | str] | str, run_dir: str, file_ending: str = '.mzML', method: str = 'block_method', mz_binning_width: float = 1.0, mz_binning_width_unit: str = 'ppm', ms_levels: List[int] = [1], sort_blocks: str = 'RT_ascending', rt_block_size: int | None = None, rt_max_length: float = 0.0, spectrum_type: str = 'automatic', rt_range: float | None = 5.0, rt_unit: str = 'scans', rt_FWHM: float = 5.0, cutoff: float = 0.01, precursor_mass_tol: float = 0.0, precursor_max_charge: int = 1, deepcopy: bool = False) → str

Merge several spectra into one spectrum (useful for MS1 spectra to amplify signals along near retention times)

Parameters:

• experiments (Union[Sequence[Union[oms.MSExperiment,str]], str]) – Input experiments

• run_dir (str) – Run directory

• file_ending (str, optional) – File ending, defaults to “.mzML”

• method (str, optional) – Method to perform merging, defaults to “block_method”

• mz_binning_width (float, optional) – m/z binning width, defaults to 1.0

• mz_binning_width_unit (str, optional) – m/z binning width unit (ppm/Da), defaults to “ppm”

• ms_levels (List[int], optional) – MS levels to consider, defaults to [1]

• sort_blocks (str, optional) – Sort blocks by rentention time, defaults to “RT_ascending”

• rt_block_size (Optional[int], optional) – Block size along retention time, defaults to None

• rt_max_length (float, optional) – Maximal length of Retention time, defaults to 0.0

• spectrum_type (str, optional) – Spectrum type determination, defaults to “automatic”

• rt_range (Optional[float], optional) – Retention time range to merge over, defaults to 5.0

• rt_unit (str, optional) – Unit to merge over, defaults to “scans”

• rt_FWHM (float, optional) – Full width at half maximum, defaults to 5.0

• cutoff (float, optional) – Cutoff value during merging, defaults to 0.01

• precursor_mass_tol (float, optional) – Percursor mass tolerance, defaults to 0.0

• precursor_max_charge (int, optional) – Maximal precursor charge, defaults to 1

• deepcopy (bool, optional) – Perform a deepcopy to relieably unlink the experiment from the input, defaults to False

Raises:

ValueError – Method needs to be in [block_method|average_tophat|average_gaussian]

Returns:

Directory with merged experiments

Return type:

str

skripts.FIA.FIA.merge_by_mz(id_df_1: DataFrame, id_df_2: DataFrame, mz_tolerance: float = 0.0001) → DataFrame

Merge dataframes by mz column.

Parameters:

• id_df_1 (pandas.DataFrame) – Input dataframe 1

• id_df_2 (pandas.DataFrame) – Input dataframe 2

• mz_tolerance (float, optional) – Tolerance of m/z deviation, defaults to 1e-04

Returns:

_description_

Return type:

pandas.DataFrame

skripts.FIA.FIA.merge_compounds(path_to_tsv: str) → DataFrame

Joins entries with equal Mass and SumFormula. Links CompoundName with: ;. Links rest with: ,.

Parameters:

path_to_tsv (str) – Path to TSV

Returns:

Merged compounds

Return type:

pd.DataFrame

skripts.FIA.FIA.merge_experiment(experiment: _MSExperimentDF | str, method: str = 'block_method', mz_binning_width: float = 1.0, mz_binning_width_unit: str = 'ppm', ms_levels: List[int] = [1], sort_blocks: str = 'RT_ascending', rt_block_size: int | None = None, rt_max_length: float = 0.0, spectrum_type: str = 'automatic', rt_range: float | None = 5.0, rt_unit: str = 'scans', rt_FWHM: float = 5.0, cutoff: float = 0.01, precursor_mass_tol: float = 0.0, precursor_max_charge: int = 1, deepcopy: bool = False) → _MSExperimentDF

Merge several spectra into one spectrum (useful for MS1 spectra to amplify signals)

Parameters:

• experiment (Union[oms.MSExperiment, str]) – Input experiment

• method (str, optional) – Method to perform merging, defaults to “block_method”

• mz_binning_width (float, optional) – m/z binning width, defaults to 1.0

• mz_binning_width_unit (str, optional) – m/z binning width unit (ppm/Da), defaults to “ppm”

• ms_levels (List[int], optional) – MS levels to consider, defaults to [1]

• sort_blocks (str, optional) – Sort blocks by rentention time, defaults to “RT_ascending”

• rt_block_size (Optional[int], optional) – Block size along retention time, defaults to None

• rt_max_length (float, optional) – Maximal length of Retention time, defaults to 0.0

• spectrum_type (str, optional) – Spectrum type determination, defaults to “automatic”

• rt_range (Optional[float], optional) – Retention time range to merge over, defaults to 5.0

• rt_unit (str, optional) – Unit to merge over, defaults to “scans”

• rt_FWHM (float, optional) – Full width at half maximum, defaults to 5.0

• cutoff (float, optional) – Cutoff value during merging, defaults to 0.01

• precursor_mass_tol (float, optional) – Percursor mass tolerance, defaults to 0.0

• precursor_max_charge (int, optional) – Maximal precursor charge, defaults to 1

• deepcopy (bool, optional) – Perform a deepcopy to relieably unlink the experiment from the input, defaults to False

Raises:

ValueError – Method needs to be in [block_method|average_tophat|average_gaussian]

Returns:

Merged experiment

Return type:

pyopenms.MSExperiment

skripts.FIA.FIA.merge_experiments(experiments: Sequence[_MSExperimentDF | str] | str, run_dir: str, file_ending: str = '.mzML', method: str = 'block_method', mz_binning_width: float = 1.0, mz_binning_width_unit: str = 'ppm', ms_levels: List[int] = [1], sort_blocks: str = 'RT_ascending', rt_block_size: int | None = None, rt_max_length: float = 0.0, spectrum_type: str = 'automatic', rt_range: float | None = 5.0, rt_unit: str = 'scans', rt_FWHM: float = 5.0, cutoff: float = 0.01, precursor_mass_tol: float = 0.0, precursor_max_charge: int = 1, deepcopy: bool = False) → _MSExperimentDF

Merge several spectra into one spectrum (useful for MS1 spectra to amplify signals along near retention times). Combines all spectra of given experiments into one experiment to merge over.

Parameters:

• experiments (Union[Sequence[Union[oms.MSExperiment,str]], str]) – Input experiments

• run_dir (str) – Run directory

• file_ending (str, optional) – File ending, defaults to “.mzML”

• method (str, optional) – Method to perform merging, defaults to “block_method”

• mz_binning_width (float, optional) – m/z binning width, defaults to 1.0

• mz_binning_width_unit (str, optional) – m/z binning width unit (ppm/Da), defaults to “ppm”

• ms_levels (List[int], optional) – MS levels to consider, defaults to [1]

• sort_blocks (str, optional) – Sort blocks by rentention time, defaults to “RT_ascending”

• rt_block_size (Optional[int], optional) – Block size along retention time, defaults to None

• rt_max_length (float, optional) – Maximal length of Retention time, defaults to 0.0

• spectrum_type (str, optional) – Spectrum type determination, defaults to “automatic”

• rt_range (Optional[float], optional) – Retention time range to merge over, defaults to 5.0

• rt_unit (str, optional) – Unit to merge over, defaults to “scans”

• rt_FWHM (float, optional) – Full width at half maximum, defaults to 5.0

• cutoff (float, optional) – Cutoff value during merging, defaults to 0.01

• precursor_mass_tol (float, optional) – Percursor mass tolerance, defaults to 0.0

• precursor_max_charge (int, optional) – Maximal precursor charge, defaults to 1

• deepcopy (bool, optional) – Perform a deepcopy to relieably unlink the experiment from the input, defaults to False

Raises:

ValueError – Method needs to be in [block_method|average_tophat|average_gaussian]

Returns:

Directory with merged experiments

Return type:

str

skripts.FIA.FIA.merge_mz_tolerance(comb_df: DataFrame, charge: int = 1, tolerance: float = 0.001, binned: bool = False) → DataFrame

Weighted average of m/z values that are within absolute tolerance of a row in the primary dataframe.

Parameters:

• comb_df (pandas.DataFrame) – Dataframe for merging

• charge (int, optional) – Theoretical charge, defaults to 1

• tolerance (float, optional) – Tolerance of m/z difference, defaults to 1e-3

• binned (bool, optional) – Values already binned (simpler process), defaults to False

Returns:

Merged Dataframe

Return type:

pandas.DataFrame

skripts.FIA.FIA.mnx_to_oms(df: DataFrame) → DataFrame

Turns a dataframe from MetaNetX into the required format by pyopenms for feature detection.

Parameters:

df (pandas.DataFrame) – Parameters Dataframe

Returns:

DataFrame with essential information.

Return type:

pandas.DataFrame

skripts.FIA.FIA.normalize_spectra(experiment: _MSExperimentDF | str, normalization_method: str = 'to_one', deepcopy: bool = False) → _MSExperimentDF

Normalizes spectra by specified method.

Parameters:

• experiment (Union[oms.MSExperiment, str]) – Input experiment

• normalization_method (Normalization method in [to_TIC | to_one] , optional) – _description_, defaults to “to_one”

• deepcopy (bool, optional) – Perform a deepcopy to relieably unlink the experiment from the input, defaults to False

Returns:

Normlaized experiment along spectra

Return type:

oms.MSExperiment

skripts.FIA.FIA.plot_feature_map_rt_alignment(ordered_feature_maps: list, legend: bool = False) → None

Plot feature map retention time alignment

Parameters:

• ordered_feature_maps (list) – Listof feature maps

• legend (bool, optional) – Display legend, defaults to False

skripts.FIA.FIA.plot_features_3D(feature_map: _FeatureMapDF, plottype: str = 'scatter') → DataFrame

Represents found features in 3D

Parameters:

• feature_map (oms.FeatureMap) – Input feature map

• plottype (str, optional) – Type of plot [scatter|surface|line], defaults to “scatter”

Raises:

ValueError – Use [‘surface’,’scatter’,’line’]

Returns:

Dataframe with 3d features

Return type:

pandas.DataFrame

skripts.FIA.FIA.plot_id_df(id_df: DataFrame, x: str = 'RT', y: str = 'mz') → None

Scatterplot dataframe with identified metabolites.

Parameters:

• id_df (pd.DataFrame) – Input dataframe

• x (str, optional) – x-axis, defaults to “RT”

• y (str, optional) – y-axis, defaults to “mz”

skripts.FIA.FIA.plot_mass_traces(mass_traces, sel=[0, 100], x: str = 'rt', y: str = 'mz', z: str = 'int', threed: bool = True)

Plot mass traces along 3d plot.

Parameters:

• mass_traces (list) – List of mass traces

• sel (list, optional) – Selection of convex hull points, defaults to [0,100]

• x (str, optional) – x-axis column, defaults to “rt”

• y (str, optional) – y-axis column, defaults to “mz”

• z (str, optional) – z-axis column, defaults to “int”

• threed (bool, optional) – 3D plot, defaults to True

Returns:

Plot

Return type:

plotly-express plot

skripts.FIA.FIA.print_params(p)

Print parameters of pyopenms class.

Parameters:

p (dict-like) – Parameters

skripts.FIA.FIA.quick_plot(spectrum: MSSpectrum, xlim: List[float] | None = None, ylim: List[float] | None = None, plottype: str = 'line', log: List[str] = []) → Figure

Shows a plot of a spectrum between the defined borders

Parameters:

• spectrum (pyopenms.MSSpectrum) – Input spectrum

• xlim (Optional[List[float]], optional) – x-axis limits, defaults to None

• ylim (Optional[List[float]], optional) – y-axis limits, defaults to None

• plottype (str, optional) – Type of plot [line|scatter], defaults to “line”

• log (List[str], optional) – Axes to log-transform [x,y], defaults to []

Returns:

Figure

Return type:

Figure

skripts.FIA.FIA.read_experiment(experiment_path: str, separator: str = '\t') → _MSExperimentDF

Read in MzXML or MzML File as a pyopenms experiment. If the file is in tabular format, assumes that is is in long form with two columns [“mz”, “inty”]

Parameters:

• experiment_path (str) – Path to experiment

• separator (str, optional) – Separator of data, defaults to ” “

Raises:

ValueError – The experiment must end with a valid ending.

Returns:

Experiment

Return type:

pyopenms.MSExperiment

skripts.FIA.FIA.read_feature_map_XML(path_to_featureXML: str) → _FeatureMapDF

Reads in feature Map from .featureXML file.

Parameters:

path_to_featureXML (str) – Path to featureXML file.

Returns:

Feature map

Return type:

pyopenms.FeatureMap

skripts.FIA.FIA.read_feature_maps_XML(path_to_featureXMLs: str) → list

Reads in feature Maps from file

Parameters:

path_to_featureXMLs (str) – Path to featureXML file directory.

Returns:

List of feature maps

Return type:

list

skripts.FIA.FIA.read_mnx(filepath: str) → DataFrame

Read in chem_prop.tsv file from MetaNetX

Parameters:

filepath (str) – Path to file

Returns:

DataFrame

Return type:

pd.DataFrame

skripts.FIA.FIA.separate_feature_maps_pos_neg(feature_maps: list) → list

Separate the feature maps into positively and negatively charged feature maps.

Parameters:

feature_maps (list) – Input list of feature maps

Returns:

List of list of feature maps, separates into positive and negatively charged

Return type:

list

skripts.FIA.FIA.smooth_spectra(experiment: _MSExperimentDF | str, gaussian_width: float, deepcopy: bool = False) → _MSExperimentDF

Apply a Gaussian filter to all spectra in an experiment.

Parameters:

• experiment (Union[pyopenms.MSExperiment, str]) – Input experiment

• gaussian_width (float) – Window width to apply smoothing to

• deepcopy (bool, optional) – Perform a deepcopy to relieably unlink the experiment from the input, defaults to False

Returns:

Smoothed experiment

Return type:

pyopenms.MSExperiment

skripts.FIA.FIA.sns_plot(x, y, hue=None, size=None, xlim: List[float] | None = None, ylim: List[float] | None = None, plottype: str = 'line', log: List[str] = [], sizes: Tuple[int, int] | None = (20, 20), palette: str = 'hls', figsize: Tuple[int, int] | None = (18, 5)) → None

Shows a plot of a spectrum between the defined borders.

Parameters:

• x (array-like) – x-values

• y (array-like) – x-values

• hue (all, optional) – Column to group x and y values, defaults to None

• size (float, optional) – Size of elements, defaults to None

• xlim (Optional[List[float]], optional) – x-axis limits, defaults to None

• ylim (Optional[List[float]], optional) – y-axis limits, defaults to None

• plottype (str, optional) – Type of plot [line|scatter], defaults to “line”

• log (List[str], optional) – Axes to log-transform [x,y], defaults to []

• sizes (Optional[Tuple[int, int]], optional) – Sizes of points in relation to y-value, defaults to (20,20)

• palette (str, optional) – Color palette, defaults to “hls”

• figsize (Optional[Tuple[int,int]], optional) – Figure size, defaults to (18, 5)

skripts.FIA.FIA.store_experiment(experiment_path: str, experiment: _MSExperimentDF) → None

Stores the experiment.

Parameters:

• experiment_path (str) – Path to be stored at (must end with .mzMl or .mzXML)

• experiment (pyopenms.MSExperiment) – Experiment

skripts.FIA.FIA.store_feature_maps(feature_maps: list, out_dir: str, names: list[str] | str = [], file_ending: str = '.mzML') → None

Stores the feature maps as featureXML files.

Parameters:

• feature_maps (list) – Feature Maps

• out_dir (str) – Output directory

• names (Union[list[str], str], optional) – Names of feature maps, defaults to []

• file_ending (str, optional) – Ending of file, defaults to “.mzML”

skripts.FIA.FIA.sum_spectra(experiment: _MSExperimentDF) → MSSpectrum

Sum up spectra in one experiment.

Parameters:

experiment (pyopenms.MSExperiment) – Input experiment

Returns:

Spectrum with summed intensities along m/z axis

Return type:

pyopenms.MSSpectrum

skripts.FIA.FIA.targeted_feature_detection(experiment: _MSExperimentDF | str, compound_library_file: str, mz_window: float = 5.0, rt_window: float | None = None, n_isotopes: int = 2, isotope_pmin: float = 0.01, peak_width: float = 60.0, mass_range: list = [50.0, 10000.0]) → _FeatureMapDF

Feature detection with a given metabolic table with compund library file.

Parameters:

• experiment (Union[oms.MSExperiment, str]) – Input experiment

• compound_library_file (str) – Path to compound library file to define metabolic table from.

• metab_table (list) – Metabilites table

• feature_filepath (Optional[str], optional) – Filpath to featureXML, defaults to None

• mz_window (float, optional) – m/z window width, defaults to 5.0

• rt_window (Optional[float], optional) – Retention time window width, defaults to None

• n_isotopes (int, optional) – Number of considered isotopes, defaults to 2

• isotope_pmin (float, optional) – Minimal probability of an isotope to be considered, defaults to 0.01

• peak_width (float, optional) – Standard peak width, defaults to 60.0

Returns:

Feature map

Return type:

pyopenms.FeatureMap

skripts.FIA.FIA.targeted_features_detection(in_dir: str, run_dir: str, file_ending: str, compound_library_file: str, mz_window: float = 5.0, rt_window: float = 20.0, n_isotopes: int = 2, isotope_pmin: float = 0.01, peak_width: float = 60.0, mass_range: list = [50.0, 10000.0]) → list[_FeatureMapDF]

Feature detection with a given metabolic table with compund library file.

Parameters:

• in_dir (str) – Input directory

• run_dir (str) – Run directory

• file_ending (str) – File ending

• compound_library_file (str) – Path to compound library file to define metabolic table from.

• metab_table (list) – Metabilites table

• feature_filepath (Optional[str], optional) – Filpath to featureXML, defaults to None

• mz_window (float, optional) – m/z window width, defaults to 5.0

• rt_window (Optional[float], optional) – Retention time window width, defaults to None

• n_isotopes (int, optional) – Number of considered isotopes, defaults to 2

• isotope_pmin (float, optional) – Minimal probability of an isotope to be considered, defaults to 0.01

• peak_width (float, optional) – Standard peak width, defaults to 60.0

Returns:

List of feature maps

Return type:

list[pyopenms.FeatureMap]

skripts.FIA.FIA.trim_threshold(experiment: _MSExperimentDF, threshold: float = 0.05) → _MSExperimentDF

Removes point below an absolute intensity theshold.

Parameters:

• experiment (pyopenms.MSExperiment) – Input Experiment

• threshold (float, optional) – Threshold for values to be excluded, defaults to 0.05

Returns:

Trimmed experiment

Return type:

oms.MSExperiment

skripts.FIA.FIA.trim_threshold_batch(experiments: Sequence[_MSExperimentDF | str] | str, run_dir: str, file_ending: str = '.mzML', threshold: float = 0.05, deepcopy: bool = False) → str

Removes points below an absolute intensity theshold in a batch of experiments.

Parameters:

• experiments (Union[Sequence[Union[pyopenms.MSExperiment,str]], str]) – Input Experiments (as a list of experiments, a directory, or paths to experiemnts)

• threshold (float, optional) – Threshold for values to be excluded, defaults to 0.05

• deepcopy (bool, optional) – Perform a deepcopy to relieably unlink the experiment from the input, defaults to False

Returns:

Folder with trimmed experiments

Return type:

str

skripts.FIA.FIA.untargeted_feature_detection(experiment: _MSExperimentDF | str, feature_filepath: str | None = None, mass_error_ppm: float = 5.0, noise_threshold_int: float = 3000.0, charge_lower_bound: int = 1, charge_upper_bound: int = 3, width_filtering: str = 'fixed', isotope_filtering_model='none', remove_single_traces='true', mz_scoring_by_elements='false', report_convex_hulls='true', deepcopy: bool = False) → _FeatureMapDF

Untargeted detection of features. Combines mMass trace detection, elution peak detection and feature finding.

Parameters:

• experiment (Union[oms.MSExperiment, str]) – Input experiment

• feature_filepath (Optional[str], optional) – Path to featureXML file, defaults to None

• mass_error_ppm (float, optional) – Mass error in ppm, defaults to 10.0

• noise_threshold_int (float, optional) – Noise threshold intensity, defaults to 1000.0

• charge_lower_bound (int, optional) – Lower charge bound, defaults to 1

• charge_upper_bound (int, optional) – Upper charge bound, defaults to 3

• width_filtering (str, optional) – Type of width filtering, defaults to “fixed”

• isotope_filtering_model (str, optional) – Isotope filtering model, defaults to “metabolites (2% RMS)”

• remove_single_traces (str, optional) – Remove single traces, defaults to “true”

• mz_scoring_by_elements (str, optional) – Score m/z by present elements, defaults to “false”

• report_convex_hulls (str, optional) – Report convex hulls, defaults to “true”

• deepcopy (bool, optional) – Perform a deepcopy to relieably unlink the experiment from the input, defaults to False

Returns:

Feature map

Return type:

pyopenms.FeatureMap

skripts.FIA.FIA.untargeted_features_detection(in_dir: str, run_dir: str, file_ending: str = '.mzML', mass_error_ppm: float = 10.0, noise_threshold_int: float = 1000.0, charge_lower_bound: int = 1, charge_upper_bound: int = 3, width_filtering: str = 'fixed', isotope_filtering_model: str = 'none', remove_single_traces: str = 'true', mz_scoring_by_elements: str = 'false', report_convex_hulls: str = 'true', deepcopy: bool = False) → list

Untargeted detection of features. Combines mMass trace detection, elution peak detection and feature finding.

Parameters:

• in_dir (str) – Input directory

• run_dir (str) – Run directory

• file_ending (str, optional) – File ending, defaults to “.mzML”

• mass_error_ppm (float, optional) – Mass error in ppm, defaults to 10.0

• noise_threshold_int (float, optional) – Noise threshold intensity, defaults to 1000.0

• charge_lower_bound (int, optional) – Lower charge bound, defaults to 1

• charge_upper_bound (int, optional) – Upper charge bound, defaults to 3

• width_filtering (str, optional) – Type of width filtering, defaults to “fixed”

• isotope_filtering_model (str, optional) – Isotope filtering model, defaults to “metabolites (2% RMS)”

• remove_single_traces (str, optional) – Remove single traces, defaults to “true”

• mz_scoring_by_elements (str, optional) – Score m/z by present elements, defaults to “false”

• report_convex_hulls (str, optional) – Report convex hulls, defaults to “true”

• deepcopy (bool, optional) – Perform a deepcopy to relieably unlink the experiment from the input, defaults to False

Returns:

List of feature maps

Return type:

list

skripts.FIA.FIA_oms module

Module contents