diff --git a/.cookietemple.yml b/.cookietemple.yml index a1982c1..06582fa 100644 --- a/.cookietemple.yml +++ b/.cookietemple.yml @@ -15,5 +15,5 @@ full_name: Mario Picciani email: mario.picciani@tum.de project_name: spectrum_io project_short_description: IO related functionalities for oktoberfest. -version: 0.4.1 +version: 0.4.2 license: MIT diff --git a/.flake8 b/.flake8 index 151e3b8..7b0c786 100644 --- a/.flake8 +++ b/.flake8 @@ -9,4 +9,5 @@ per-file-ignores = noxfile.py:DAR101 spectrum_io/raw/thermo_raw.py:S603,S404 spectrum_io/raw/msraw.py:S405,S314 - docs/conf.py:S404,S607,S603 + spectrum_io/d/masterSpectrum.py:C901 + docs/conf.py:S404,S607,S603 diff --git a/.github/release-drafter.yml b/.github/release-drafter.yml index 4f014df..748c263 100644 --- a/.github/release-drafter.yml +++ b/.github/release-drafter.yml @@ -1,5 +1,5 @@ -name-template: "0.4.1 🌈" # <> -tag-template: 0.4.1 # <> +name-template: "0.4.2 🌈" # <> +tag-template: 0.4.2 # <> exclude-labels: - "skip-changelog" diff --git a/cookietemple.cfg b/cookietemple.cfg index 7fc5189..831325c 100644 --- a/cookietemple.cfg +++ b/cookietemple.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 0.4.1 +current_version = 0.4.2 [bumpversion_files_whitelisted] init_file = spectrum_io/__init__.py diff --git a/docs/conf.py b/docs/conf.py index a042c6b..f44e3da 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -53,9 +53,9 @@ # the built documents. # # The short X.Y version. -version = "0.4.1" +version = "0.4.2" # The full version, including alpha/beta/rc tags. -release = "0.4.1" +release = "0.4.2" # The language for content autogenerated by Sphinx. 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It then reads the temporary file as a DataFrame, + modifies the DataFrame by adding SCAN_NUMBER and dropping specified columns before returning it. + + :param intensities: Input data used to perform binning. + :param mzs: Path where the temporary file will be exported. + :param ignore_charges: indicating whether charges should be ignored during binning. + :return: Tuple containing the sorted list of fragment mzs and associated intensities + """ + ms = MasterSpectrum() + ms.load_from_tims(intensities, mzs, ignore_charges) + + mzs_out = [mp.mz for key in ms.spectrum[0].keys() for mp in ms.spectrum[0][key]] + intensities_out = [mp.intensity for key in ms.spectrum[0].keys() for mp in ms.spectrum[0][key]] + + return mzs_out, intensities_out + + +def aggregate_timstof(raw_spectra: pd.DataFrame) -> pd.DataFrame: + """ + Combine spectra from the provided pd.DataFrame and perform binning on chunks. + + This function splits the input pd.DataFrame into chunks, performs binning on each chunk of data, + merges the binning results with the original data, processes the combined spectra, and returns + the combined and processed spectra as a pd.DataFrame. + + :param raw_spectra: pd.DataFrame containing spectra information. + :return: pd.DataFrame containing combined and processed spectra. + """ + for i, (combined_intensities, combined_mzs) in tqdm( + enumerate(zip(raw_spectra["INTENSITIES"], raw_spectra["MZ"])), + total=len(raw_spectra), + desc="Aggregating spectra", + ): + mz, intensity = binning(combined_mzs, combined_intensities, True) + raw_spectra.at[i, "INTENSITIES"] = intensity + raw_spectra.at[i, "MZ"] = mz + + return raw_spectra + + +def read_timstof(hdf_file: Path, scan_to_precursor_map: pd.DataFrame) -> pd.DataFrame: + """ + Read selected spectra from a given timstof hdf file. + + This function queries a given hdf file for spectra that are provided within a scan to precursor map. + #TODO elaborate + + :param hdf_file: Path to hdf file containing spectra + :param scan_to_precursor_map: Dataframe containing metadata to select spectra + + :return: Dataframe containing the relevant spectra read from the hdf file + """ + # preparation of filter + df_frame_group = ( + scan_to_precursor_map[["FRAME", "PRECURSOR"]] + .drop_duplicates() + .groupby("FRAME", as_index=False) + .agg( + { + "PRECURSOR": tuple, + } + ) + .groupby("PRECURSOR", as_index=False) + .agg({"FRAME": tuple}) + ) + + # load filtered stuff + data = alphatims.bruker.TimsTOF(str(hdf_file), slice_as_dataframe=False) + + raw_idx = [] + for frames, precursors in zip(df_frame_group["FRAME"], df_frame_group["PRECURSOR"]): + raw_idx.extend(data[frames, :, precursors]) + + # read spectra + df = data.as_dataframe( + raw_idx, + raw_indices=False, + frame_indices=True, + scan_indices=True, + quad_indices=False, + tof_indices=False, + precursor_indices=True, + rt_values=True, + rt_values_min=False, + mobility_values=True, + quad_mz_values=False, + push_indices=False, + mz_values=True, + intensity_values=True, + corrected_intensity_values=False, + raw_indices_sorted=False, + ) + df.columns = ["FRAME", "SCAN", "PRECURSOR", "RETENTION_TIME", "INV_ION_MOBILITY", "MZ", "INTENSITIES"] + + # aggregation + df_combined_grouped = ( + df.merge( + scan_to_precursor_map[ + ["SCAN_NUM_BEGIN", "SCAN_NUM_END", "PRECURSOR", "FRAME", "COLLISION_ENERGY"] + ].drop_duplicates() + ) + .query("SCAN_NUM_BEGIN <= SCAN <= SCAN_NUM_END") # can probably be skipped + .groupby(["PRECURSOR", "FRAME"], as_index=False) # aggregate fragments per precursor in FRAME + .agg( + { + "INTENSITIES": list, + "MZ": list, + "RETENTION_TIME": "first", + "COLLISION_ENERGY": "first", + "INV_ION_MOBILITY": "first", + } + ) + .merge(scan_to_precursor_map.reset_index()) + .groupby("SCAN_NUMBER", as_index=False) # aggregate PRECURSORS for same SCAN_NUMBER + .agg( + COLLISION_ENERGY=("COLLISION_ENERGY", "median"), + INTENSITIES=("INTENSITIES", lambda x: [item for sublist in x for item in sublist]), + MZ=("MZ", lambda x: [item for sublist in x for item in sublist]), + RETENTION_TIME=("RETENTION_TIME", "median"), + median_INV_ION_MOBILITY=("INV_ION_MOBILITY", "median"), + ) + ) + + return df_combined_grouped + + +def convert_d_hdf( + input_path: Union[Path, str], + output_path: Union[Path, str], +): + """ + Convert a bruker d folder to hdf format. + + # TODO long description + :param input_path: Path to the d folder to be converted + :param output_path: Path to the desired output location of the converted hdf file + """ + if isinstance(output_path, str): + output_path = Path(input_path) + if output_path.is_file(): + logger.info(f"Found converted file at {output_path}, skipping conversion") + return + logger.info("Converting bruker d to hdf using alphatims...") + data = alphatims.bruker.TimsTOF(str(input_path)) + data.save_as_hdf(directory=str(output_path.parent), file_name=str(output_path.name)) + + +def read_and_aggregate_timstof(source: Path, tims_meta_file: Path) -> pd.DataFrame: + """ + Read raw spectra from timstof hdf spectra file and aggregate to MS2 spectra. + + :param source: Path to the hdf file + :param tims_meta_file: Path to metadata mapping scan numbers to precursors / frames + :return: Dataframe containing the MS2 spectra + """ + scan_to_precursor_map = pd.read_csv(tims_meta_file) + raw_spectra = read_timstof(source, scan_to_precursor_map) + df_combined = aggregate_timstof(raw_spectra) + df_combined["RAW_FILE"] = source.stem + df_combined["MASS_ANALYZER"] = "TOF" + df_combined["FRAGMENTATION"] = "HCD" + + return df_combined diff --git a/spectrum_io/d/masterPeak.py b/spectrum_io/d/masterPeak.py new file mode 100644 index 0000000..2f4a76b --- /dev/null +++ b/spectrum_io/d/masterPeak.py @@ -0,0 +1,175 @@ +from math import ceil +from typing import TypeVar + +from .peak import Peak + +# import pyximport; pyximport.install() +# from mgf_filter.cython.mat import ceil + + +MasterPeakT = TypeVar("MasterPeakT", bound="MasterPeak") + + +class MasterPeak(Peak): + """Container class for a aggregated, summed up fragment peak.""" + + def __init__(self, peak: Peak): + """ + Contructor for a master peak. + + # TODO provide details + + :param peak: a peak object # TODO provide details + """ + # right and counts are calculated based on update func + self.right = 0 + self.counts = 1 + self.rel_intensity_ratio = 0.0 + # ratio is set to an actual value first time by comparing a spectrum to another spectrum + self.counts_ratio = 0.0 + self.mz_origin = peak.mz + super().__init__(peak.mz, peak.intensity, peak.delta_function, meta=peak.meta) + # update does not have to be called, because constructor + # of peak calls update of MasterPeak + # self.update() + + def __eq__(self, other: MasterPeakT) -> bool: # type: ignore + """ + Reports true if both master peaks have the same member variables! + + :param other: the other master peak object to compare this object with + :return: whether or not the two objects are equal + """ + if self.__dict__ == other.__dict__: + return True + else: + if ( + (self.counts == other.counts) + & (self.mz == other.mz) + & (self.left == other.left) + & (self.right == other.right) + & (self.mz_origin == other.mz_origin) + ): + return True + else: + return False + + def __ne__(self, other: MasterPeakT) -> bool: # type: ignore + """ + Reports true if any member variable differs between the two master peaks! + + :param other: the other master peak object to compare this object with + :return: whether or not the two objects are equal + """ + if self.__dict__ != other.__dict__: + if ( + (self.counts == other.counts) + & (self.mz == other.mz) + & (self.left == other.left) + & (self.right == other.right) + & (self.mz_origin == other.mz_origin) + ): + return False + else: + return True + else: + return True # TODO check if this is really true, dicts equal means the objects should be equal + + def __str__(self) -> str: + """ + Overrides the default str method to provide information about member variables. + + :return: string representation of this object + """ + return ( + "mz: " + + str(self.mz) + + "\n" + + "intensity: " + + str(self.intensity) + + "\n" + + "left: " + + str(self.left) + + "\n" + + "right: " + + str(self.right) + + "\n" + + "counts: " + + str(self.counts) + + "\n" + + "rel_intensity_ratio: " + + str(self.rel_intensity_ratio) + + "\n" + + "counts_ratio: " + + str(self.counts_ratio) + + "\n" + + "origin: " + + str(self.mz_origin) + ) + + def update(self): + """Calculates delta, left and right.""" + self.delta = self.delta_function(self.mz) + self.left = self.mz - self.delta + self.right = self.mz + self.delta + self.ceiled_key = ceil(self.left) + + def is_inside(self, peak: Peak) -> bool: + """ + Reports true if tested peak is inside window. + + :param peak: a peak object to check + + :return: whether the peak's mz is within left and right border + """ + return (self.left < peak.mz) and (self.right > peak.mz) + + def is_inside_mz(self, mz: float) -> bool: + """ + Reports true if tested mz is inside window. + + :param mz: the mz to check for + + :return: whether mz is within left and right border + """ + return (self.left < mz) and (self.right > mz) + + def add(self, peak: Peak): + """ + Default_counts must be overwritten if more than one peak is added (multimerge). + + :param peak: a peak object to be added to this master peak. + """ + self.mz = (self.mz * self.intensity + peak.mz * peak.intensity) / (peak.intensity + self.intensity) + self.intensity = self.intensity + peak.intensity + self.counts += peak.counts + self.update() + + def smaller(self, peak: Peak) -> bool: + """ + Report true if MasterPeak window is below tested peak # TODO make this a magic function. + + :param peak: the other peak to compare to + :return: wheter or not the peak is smaller to this object + """ + return self.right < peak.mz + + def greater(self, peak: Peak): + """ + Report true if MasterPeak window is above tested peak # TODO make this a magic function. + + :param peak: the other peak to compare to + :return: wheter or not the peak is greater to this object + """ + return self.left > peak.mz + + def recalculate_ratio(self, mp: MasterPeakT): + """ + Recalculate the ratio # TODO details. + + :param mp: the other master peak object + """ + rel_ratio = (self.intensity / self.counts) / (mp.intensity / mp.counts) + self.rel_intensity_ratio = rel_ratio + rel_counts_ratio = self.counts / mp.counts + self.counts_ratio = rel_counts_ratio diff --git a/spectrum_io/d/masterSpectrum.py b/spectrum_io/d/masterSpectrum.py new file mode 100644 index 0000000..9e65822 --- /dev/null +++ b/spectrum_io/d/masterSpectrum.py @@ -0,0 +1,261 @@ +import csv +from math import ceil +from typing import Callable, List, Optional + +from sortedcontainers import SortedDict, SortedList + +from .masterPeak import MasterPeak +from .peak import Peak + +""" +def merge_func(offset: int, idx: int): + master_peak_bordering = self.spectrum[charge][key][idx + offset] + get_idx_master_peak = self.spectrum[charge][key][idx] + # left idx -1 : idx +1 # right idx : idx+2 + # left offset = -1 -> +2 -1 = 1 + # right offset = 0 -> +2 +0 = 2 + del self.spectrum[charge][key][ + idx + offset : idx + 2 + offset + ] # delete is slicing, therefore one more + if len(self.spectrum[charge][key]) == 0: + del self.spectrum[charge][key] + get_idx_master_peak.add(master_peak_bordering) + get_idx_master_peak.add(peak) + self.multimerged += 1 + if get_idx_master_peak.key() not in self.spectrum[charge]: + self.spectrum[charge][get_idx_master_peak.key()] = SortedList(key=lambda i: i.left) + self.spectrum[charge][get_idx_master_peak.key()].add(get_idx_master_peak) +""" + + +def _calculate_delta_by_ppm(ppm: int) -> Callable: + def fix_ppm(mz: float): + return ppm * float(mz) / (pow(10, 6)) + + return fix_ppm + + +def _calculate_relative_intensity(a_intensity: List[int]) -> List[float]: + max_intensity = max(a_intensity) + return [x / max_intensity for x in a_intensity] + + +class MasterSpectrum: + """ + Container class for a Spectrum. + + Master spectrum takes peaks and converts them to master peaks. If there are no master peaks + to be inserted spectrum is {charge of spectrum: {bins: MP}}. + """ + + def __init__(self): + """Constructor for MasterSpectrum.""" + self.spectrum = {} + self.merged = 0 + self.appended = 0 + self.multimerged = 0 # within one insertion 3 peaks are merged + + def binary(self, peak: Peak, imin: int, imax: int, charge: int): + """ + Binary search. + + :param peak: #TODO + :param imin: the minimum search position + :param imax: the max search position + :param charge: defines in which masterspectrum to search + :return: tuple with the following members # TODO proper documentation: + position of insert, -1 if can not be added to any peak + add peak from left or right bin + must left peak also be added (merge case) + right peak also be added (think about 3 peaks and a merge between 2 and 3) + """ + key = peak.key() + imid = int(ceil((imax + imin) / 2)) + if imax < imin: + exist_left_bin = key - 1 in self.spectrum[charge].keys() + exist_right_bin = key + 1 in self.spectrum[charge].keys() + + if exist_left_bin and self.spectrum[charge][key - 1][-1].is_inside(peak): + return -1, -1, False, False + if exist_right_bin and self.spectrum[charge][key + 1][0].is_inside(peak): + return -1, 1, False, False + return -1, 0, False, False + + # search must go on + mspeak_in_bin = self.spectrum[charge][key][imid] + if mspeak_in_bin.greater(peak): + return self.binary(peak, imin, imid - 1, charge) + elif mspeak_in_bin.smaller(peak): + return self.binary(peak, imid + 1, imax, charge) + # search results in peak that should be added + else: + if imid == 0: + exist_left_bin = key - 1 in self.spectrum[charge] + exist_right_bin = key + 1 in self.spectrum[charge] + + if exist_left_bin and self.spectrum[charge][key - 1][-1].is_inside(peak): + return 0, -1, False, False + elif exist_right_bin and self.spectrum[charge][key + 1][0].is_inside(peak): + return 0, 1, False, False + return 0, 0, False, False + + else: # peak is somewhere between 1 and last + # imid -1 exists alway + is_last_entry = len(self.spectrum[charge][key]) - 1 == imid + exists_bigger_peak = not (is_last_entry) + if exists_bigger_peak: + if self.spectrum[charge][key][imid - 1].is_inside(peak): + return imid, 0, True, False + if self.spectrum[charge][key][imid + 1].is_inside(peak): + return imid, 0, False, True + return imid, 0, False, False + else: # is last entry + exist_right_bin = key + 1 in self.spectrum[charge] + if self.spectrum[charge][key][imid - 1].is_inside(peak): + return imid, 0, True, False + elif exist_right_bin and self.spectrum[charge][key + 1][0].is_inside(peak): + return imid, 1, False, False + return imid, 0, False, False + + def add(self, peak, charge: int = 0): + """ + Add charges to peaks. + + Charge is by default 0, so if a spectrum should be summed without including charge information + it is defaulted to 0. + + :param peak: TODO + :param charge: TODO + """ + key = peak.key() + if charge not in self.spectrum: + self.spectrum[charge] = SortedDict() + + if key in self.spectrum[charge]: + idx, bin_to_ack, should_merge_left_peak, should_merge_right_peak = self.binary( + peak, 0, len(self.spectrum[charge][key]) - 1, charge + ) + if idx == -1: # does not have to react to merge cases! + if bin_to_ack == 0: + self.appended += 1 + if type(peak).__name__ == "Peak": + self.spectrum[charge][key].add(MasterPeak(peak)) + else: + self.spectrum[charge][key].add(peak) + elif bin_to_ack == -1: + if len(self.spectrum[charge][key]) == 0: + del self.spectrum[charge][key] + get_master_peak_left_bin = self.spectrum[charge][key - 1][-1] + del self.spectrum[charge][key - 1][-1] + if len(self.spectrum[charge][key - 1]) == 0: + del self.spectrum[charge][key - 1] + get_master_peak_left_bin.add(peak) + self.merged += 1 + if get_master_peak_left_bin.key() not in self.spectrum[charge]: + self.spectrum[charge][get_master_peak_left_bin.key()] = SortedList(key=lambda i: i.left) + self.spectrum[charge][get_master_peak_left_bin.key()].add(get_master_peak_left_bin) + else: # +1 + if len(self.spectrum[charge][key]) == 0: + del self.spectrum[charge][key] + get_master_peak_right_bin = self.spectrum[charge][key + 1][0] + del self.spectrum[charge][key + 1][0] + if len(self.spectrum[charge][key + 1]) == 0: + del self.spectrum[charge][key + 1] + get_master_peak_right_bin.add(peak) + self.merged += 1 + if get_master_peak_right_bin.key() not in self.spectrum[charge]: + self.spectrum[charge][get_master_peak_right_bin.key()] = SortedList(key=lambda i: i.left) + self.spectrum[charge][get_master_peak_right_bin.key()].add(get_master_peak_right_bin) + + else: # idx != -1 + if bin_to_ack == 0: + if should_merge_left_peak: + get_left_master_peak = self.spectrum[charge][key][idx - 1] + get_idx_master_peak = self.spectrum[charge][key][idx] + del self.spectrum[charge][key][idx - 1 : idx + 1] # delete is slicing, therefore one more + if len(self.spectrum[charge][key]) == 0: + del self.spectrum[charge][key] + get_idx_master_peak.add(get_left_master_peak) + get_idx_master_peak.add(peak) + self.multimerged += 1 + if get_idx_master_peak.key() not in self.spectrum[charge]: + self.spectrum[charge][get_idx_master_peak.key()] = SortedList(key=lambda i: i.left) + self.spectrum[charge][get_idx_master_peak.key()].add(get_idx_master_peak) + elif should_merge_right_peak: + get_idx_master_peak = self.spectrum[charge][key][idx] + get_right_master_peak = self.spectrum[charge][key][idx + 1] + del self.spectrum[charge][key][idx : idx + 2] # delete is slicing, therefore one more + if len(self.spectrum[charge][key]) == 0: + del self.spectrum[charge][key] + get_idx_master_peak.add(get_right_master_peak) + get_idx_master_peak.add(peak) + self.multimerged += 1 + if get_idx_master_peak.key() not in self.spectrum[charge]: + self.spectrum[charge][get_idx_master_peak.key()] = SortedList(key=lambda i: i.left) + self.spectrum[charge][get_idx_master_peak.key()].add(get_idx_master_peak) + else: # case idx = 0, 0, False, False + get_idx_master_peak = self.spectrum[charge][key][idx] + del self.spectrum[charge][key][idx] # delete is slicing, therefore one more + if len(self.spectrum[charge][key]) == 0: + del self.spectrum[charge][key] + get_idx_master_peak.add(peak) + self.merged += 1 + if get_idx_master_peak.key() not in self.spectrum[charge]: + self.spectrum[charge][get_idx_master_peak.key()] = SortedList(key=lambda i: i.left) + self.spectrum[charge][get_idx_master_peak.key()].add(get_idx_master_peak) + elif bin_to_ack == -1: # idx != -1 + # 0, -1, F, F + get_idx_master_peak = self.spectrum[charge][key][idx] + get_master_peak_before = self.spectrum[charge][key - 1][-1] + del self.spectrum[charge][key][idx] + if len(self.spectrum[charge][key]) == 0: + del self.spectrum[charge][key] + del self.spectrum[charge][key - 1][-1] + if len(self.spectrum[charge][key - 1]) == 0: + del self.spectrum[charge][key - 1] + get_idx_master_peak.add(get_master_peak_before) + get_idx_master_peak.add(peak) + self.merged += 1 + if get_idx_master_peak.key() not in self.spectrum[charge]: + self.spectrum[charge][get_idx_master_peak.key()] = SortedList(key=lambda i: i.left) + self.spectrum[charge][get_idx_master_peak.key()].add(get_idx_master_peak) + else: # bin_to_ack == 1, idx != -1 + get_idx_master_peak = self.spectrum[charge][key][idx] + get_master_peak_after = self.spectrum[charge][key + 1][0] + del self.spectrum[charge][key][idx] + if len(self.spectrum[charge][key]) == 0: + del self.spectrum[charge][key] + del self.spectrum[charge][key + 1][0] + if len(self.spectrum[charge][key + 1]) == 0: + del self.spectrum[charge][key + 1] + get_idx_master_peak.add(get_master_peak_after) + get_idx_master_peak.add(peak) + self.merged += 1 + if get_idx_master_peak.key() not in self.spectrum[charge]: + self.spectrum[charge][get_idx_master_peak.key()] = SortedList(key=lambda i: i.left) + self.spectrum[charge][get_idx_master_peak.key()].add(get_idx_master_peak) + else: + self.spectrum[charge][key] = SortedList(key=lambda i: i.left) + self.add(peak, charge) + + def load_from_tims( + self, intensities: List[int], mzs: List[float], ignore_charges: bool, delta_func: Optional[Callable] = None + ): + """ + Load data from tims and create master spectrum. + + :param intensities: list of intensities of peaks of individual spectra to sum + :param mzs: list of mzs of peaks of individual spectra to sum + :param ignore_charges: whether to ignore charges when summing up peaks + :param delta_func: a callable to calculate the mass tolerance window + :raises NotImplementedError: If ignore_charges is set to False + """ + rel_int = _calculate_relative_intensity(intensities) + if delta_func is None: + delta_func = _calculate_delta_by_ppm(40) + for m, i in zip(mzs, rel_int): + p = Peak(float(m), float(i), delta_func) + if ignore_charges: + self.add(p, 0) + else: + raise NotImplementedError("Adding up intensities using precursor charge is not supported.") diff --git a/spectrum_io/d/peak.py b/spectrum_io/d/peak.py new file mode 100644 index 0000000..1c78fe5 --- /dev/null +++ b/spectrum_io/d/peak.py @@ -0,0 +1,56 @@ +import math +from typing import Any, Callable, List, Optional + +# import pyximport; pyximport.install() +# from mgf_filter.cython.mat import ceil + + +class Peak: + """Container class for a single fragment peak.""" + + def __init__(self, mz: float, intensity: float, delta_function: Callable, meta: Optional[List[Any]] = None): + """ + Contructor for a single fragment peak. + + A fragment peak is contructed from an mz and intensity value, as well as a delta function providing a + function to calculate the mass tolerance window and an additional metadata list # TODO provide details + :param mz: mass to charge ratio of peak + :param intensity: relative intensity of the peak + :param delta_function: callable to calculate mass tolerance window + :param meta: metadata list + """ + self.mz = mz + self.intensity = intensity + self.left = 0.0 + self.delta_function = delta_function + self.counts = 1 + self.meta = meta if meta is not None else [] + self.update() + + def __str__(self): + """Overrides the default str method to provide information about mz, intensity, left border, and delta.""" + return ( + "mz: " + + str(self.mz) + + "\n" + + "intensity: " + + str(self.intensity) + + "\n" + + "left: " + + str(self.left) + + "\n" + + "delta: " + + str(self.delta) + ) + + def update(self): + """Updates delta and calculate left border.""" + # left is needed for key() + # function will be overwritten + self.delta = self.delta_function(self.mz) + self.left = self.mz - self.delta + self.ceiled_key = math.ceil(self.left) + + def key(self): + """Provides access to the ceiled key, which is ceil rounded left border.""" + return self.ceiled_key diff --git a/spectrum_io/file/__init__.py b/spectrum_io/file/__init__.py index 2038938..35dc193 100644 --- a/spectrum_io/file/__init__.py +++ b/spectrum_io/file/__init__.py @@ -1,4 +1,5 @@ """Initialize logger.""" + import logging from . import csv, hdf5 diff --git a/spectrum_io/raw/__init__.py b/spectrum_io/raw/__init__.py index b835314..5a2da46 100644 --- a/spectrum_io/raw/__init__.py +++ b/spectrum_io/raw/__init__.py @@ -1,4 +1,5 @@ """Init raw.""" + import logging from .thermo_raw import ThermoRaw diff --git a/spectrum_io/search_result/__init__.py b/spectrum_io/search_result/__init__.py index 53e4dd0..8acd3ff 100644 --- a/spectrum_io/search_result/__init__.py +++ b/spectrum_io/search_result/__init__.py @@ -1,4 +1,5 @@ """Initialize seach result.""" + from .mascot import Mascot from .maxquant import MaxQuant from .msfragger import MSFragger diff --git a/spectrum_io/search_result/mascot.py b/spectrum_io/search_result/mascot.py index 9bfb51a..3468868 100644 --- a/spectrum_io/search_result/mascot.py +++ b/spectrum_io/search_result/mascot.py @@ -15,17 +15,15 @@ class Mascot(SearchResults): """Handle search results from Mascot.""" - @staticmethod - def read_result(path: Union[str, Path], tmt_labeled: str) -> pd.DataFrame: + def read_result(self, tmt_labeled: str) -> pd.DataFrame: """ Function to read a mascot msf file and perform some basic formatting. - :param path: path to msms.txt to read :param tmt_labeled: tmt label as str :return: pd.DataFrame with the formatted data """ logger.info("Reading mascot msf file") - connection = sqlite3.connect(path) + connection = sqlite3.connect(self.path) # cursor = connection.cursor() # cursor.execute("SELECT name FROM sqlite_master WHERE type='table';") df = pd.read_sql("SELECT * FROM MSnSpectrumInfo", connection)[ diff --git a/spectrum_io/search_result/maxquant.py b/spectrum_io/search_result/maxquant.py index e165e71..161c9da 100644 --- a/spectrum_io/search_result/maxquant.py +++ b/spectrum_io/search_result/maxquant.py @@ -14,6 +14,19 @@ class MaxQuant(SearchResults): """Handle search results from MaxQuant.""" + def __init__(self, path: Union[str, Path]): + """ + Init Searchresults object. + + :param path: path to file + """ + if isinstance(path, str): + path = Path(path) + + if path.is_file() and path.name == "msms.txt": + path = path.parent + self.path = path + @staticmethod def add_tmt_mod(mass: float, seq: str, unimod_tag: str) -> float: """ @@ -28,18 +41,16 @@ def add_tmt_mod(mass: float, seq: str, unimod_tag: str) -> float: mass += num_of_tmt * c.MOD_MASSES[f"{unimod_tag}"] return mass - @staticmethod - def read_result(path: Union[str, Path], tmt_labeled: str) -> pd.DataFrame: + def read_result(self, tmt_labeled: str) -> pd.DataFrame: """ Function to read a msms txt and perform some basic formatting. - :param path: path to msms.txt to read :param tmt_labeled: tmt label as str :return: pd.DataFrame with the formatted data """ logger.info("Reading msms.txt file") df = pd.read_csv( - path, + self.path / "msms.txt", usecols=lambda x: x.upper() in [ "RAW FILE", @@ -108,3 +119,30 @@ def update_columns_for_prosit(df: pd.DataFrame, tmt_labeled: str) -> pd.DataFram df["PEPTIDE_LENGTH"] = df["SEQUENCE"].apply(lambda x: len(x)) return df + + def generate_internal_timstof_metadata(self): + """ + Load information files required for correct aggregation of spectra in timsTOF experiments. + + :return: dataframe containing the columns RAW_FILE, SCANNUMBER, PRECURSOR, FRAME, SCANNUMBEGIN, SCANNUMEND, CollisionEnergy + """ + df_msms = pd.read_csv(self.path / "msms.txt", sep="\t", usecols=["Raw file", "Scan number"]) + df_msms.columns = ["RAW_FILE", "SCAN_NUMBER"] + + df_precursors = pd.read_csv( + self.path / "accumulatedMsmsScans.txt", sep="\t", usecols=["Raw file", "Scan number", "PASEF precursor IDs"] + ) + df_precursors.columns = ["RAW_FILE", "SCAN_NUMBER", "PRECURSOR"] + df_precursors.query("SCAN_NUMBER in @df_msms.SCAN_NUMBER", inplace=True) + df_precursors["PRECURSOR"] = df_precursors["PRECURSOR"].str.split(";") + df_precursors = df_precursors.explode("PRECURSOR") + df_precursors["PRECURSOR"] = df_precursors["PRECURSOR"].astype("int") + + df_pasef = pd.read_csv( + self.path / "pasefMsmsScans.txt", + sep="\t", + usecols=["Raw file", "Frame", "Precursor", "ScanNumBegin", "ScanNumEnd", "CollisionEnergy"], + ) + df_pasef.columns = ["RAW_FILE", "FRAME", "PRECURSOR", "SCAN_NUM_BEGIN", "SCAN_NUM_END", "COLLISION_ENERGY"] + + return df_pasef.merge(df_precursors).sort_values(["FRAME", "PRECURSOR"]) diff --git a/spectrum_io/search_result/msfragger.py b/spectrum_io/search_result/msfragger.py index 244485b..9b4470c 100644 --- a/spectrum_io/search_result/msfragger.py +++ b/spectrum_io/search_result/msfragger.py @@ -16,25 +16,20 @@ class MSFragger(SearchResults): """Handle search results from MSFragger.""" - @staticmethod - def read_result(path: Union[str, Path], tmt_labeled: str) -> pd.DataFrame: + def read_result(self, tmt_labeled: str) -> pd.DataFrame: """ Function to read a msms txt and perform some basic formatting. - :param path: path to pepXML folder or single pepXML file to read :param tmt_labeled: tmt label as str :raises FileNotFoundError: in case the given path is neither a file, nor a directory. :return: pd.DataFrame with the formatted data """ - if isinstance(path, str): - path = Path(path) - - if path.is_file(): - file_list = [path] - elif path.is_dir(): - file_list = list(path.rglob("*.pepXML")) + if self.path.is_file(): + file_list = [self.path] + elif self.path.is_dir(): + file_list = list(self.path.rglob("*.pepXML")) else: - raise FileNotFoundError(f"{path} could not be found.") + raise FileNotFoundError(f"{self.path} could not be found.") ms_frag_results = [] for pep_xml_file in tqdm(file_list): diff --git a/spectrum_io/search_result/sage.py b/spectrum_io/search_result/sage.py index 920a33a..cd19316 100644 --- a/spectrum_io/search_result/sage.py +++ b/spectrum_io/search_result/sage.py @@ -14,18 +14,16 @@ class Sage(SearchResults): """Handle search results from Sage.""" - @staticmethod - def read_result(path: Union[str, Path], tmt_labeled: str = "") -> pd.DataFrame: + def read_result(self, tmt_labeled: str = "") -> pd.DataFrame: """ Function to read a msms tsv and perform some basic formatting. - :param path: path to msms.tsv to read :param tmt_labeled: tmt label as str :return: pd.DataFrame with the formatted data """ logger.info("Reading msms.tsv file") df = pd.read_csv( - path, + self.path, usecols=["filename", "scannr", "peptide", "charge", "hyperscore", "calcmass", "proteins"], sep="\t", ) diff --git a/spectrum_io/search_result/search_results.py b/spectrum_io/search_result/search_results.py index f1ed69d..4a38cbe 100644 --- a/spectrum_io/search_result/search_results.py +++ b/spectrum_io/search_result/search_results.py @@ -51,37 +51,39 @@ def __init__(self, path: Union[str, Path]): self.path = path @abstractmethod - def read_result(self, path: Union[str, Path], tmt_labeled: str): + def read_result(self, tmt_labeled: str): """Read result.""" raise NotImplementedError - def generate_internal(self, tmt_labeled: str, out_path: Optional[Union[str, Path]] = None) -> Path: + def generate_internal(self, tmt_labeled: str, out_path: Optional[Union[str, Path]] = None) -> pd.DataFrame: """ - Generate df and save to out_path. + Generate df and save to out_path if provided. :param out_path: path to output :param tmt_labeled: tmt label as str :return: path to output file """ if out_path is None: - out_path = self.path.with_suffix(".prosit") + # convert and return + return self.read_result(tmt_labeled) + if isinstance(out_path, str): out_path = Path(out_path) if out_path.is_file(): + # only read converted and return logger.info(f"Found search results in internal format at {out_path}, skipping conversion") - return out_path + return csv.read_file(out_path) - df = self.read_result(self.path, tmt_labeled) + # convert, save and return + df = self.read_result(tmt_labeled) csv.write_file(df, out_path) + return df - return out_path - - def read_internal(self, path: Union[str, Path]) -> pd.DataFrame: + def read_internal(self) -> pd.DataFrame: """ Read file from path. - :param path: path to file :return: dataframe after reading the file """ - return csv.read_file(path) + return csv.read_file(self.path) diff --git a/spectrum_io/spectral_library/__init__.py b/spectrum_io/spectral_library/__init__.py index bffad66..456db9e 100644 --- a/spectrum_io/spectral_library/__init__.py +++ b/spectrum_io/spectral_library/__init__.py @@ -1,4 +1,5 @@ """Initialize spectral library.""" + import logging from . import digest diff --git a/tests/test_main.py b/tests/test_main.py index 85ff73c..547d33b 100644 --- a/tests/test_main.py +++ b/tests/test_main.py @@ -1,4 +1,5 @@ """Test cases for the __main__ module.""" + import pytest from click.testing import CliRunner diff --git a/tests/unit_tests/test_bruker.py b/tests/unit_tests/test_bruker.py new file mode 100644 index 0000000..27a622e --- /dev/null +++ b/tests/unit_tests/test_bruker.py @@ -0,0 +1,9 @@ +import unittest + + +class TestBruker(unittest.TestCase): + """Test class for bruker spectra files.""" + + def test_convert_hdf(self): + """Tests the function to convert .d to hdf files. Currently passed.""" + pass diff --git a/tests/unit_tests/test_msfragger.py b/tests/unit_tests/test_msfragger.py index 6b869c8..495bd12 100644 --- a/tests/unit_tests/test_msfragger.py +++ b/tests/unit_tests/test_msfragger.py @@ -11,8 +11,8 @@ class TestMSFragger(unittest.TestCase): def test_read_result(self): """Test read_result for MSFragger.""" - msfragger = MSFragger(Path(__file__).parent / "data/") - df = msfragger.read_result(Path(__file__).parent / "data/psm.pepXML", "") + msfragger = MSFragger(Path(__file__).parent / "data/psm.pepXML") + df = msfragger.read_result("") self.assertIsInstance(df, pd.DataFrame) self.assertTrue("RAW_FILE" in df.columns) self.assertTrue("SCAN_NUMBER" in df.columns) @@ -27,10 +27,11 @@ def test_read_result(self): def test_read_msfragger(self): """Test function for reading sage results and transforming to Prosit format.""" - msfragger_output_path = Path(__file__).parent / "data" / "psm_tmt.pepXML" expected_msfragger_internal_path = Path(__file__).parent / "data" / "psm_tmt_internal.csv" - internal_search_results_df = MSFragger.read_result(msfragger_output_path, tmt_labeled="tmtpro") + internal_search_results_df = MSFragger(Path(__file__).parent / "data" / "psm_tmt.pepXML").read_result( + tmt_labeled="tmtpro" + ) expected_df = pd.read_csv(expected_msfragger_internal_path, index_col=0) pd.testing.assert_frame_equal(internal_search_results_df, expected_df) diff --git a/tests/unit_tests/test_sage.py b/tests/unit_tests/test_sage.py index d136356..a2a69e6 100644 --- a/tests/unit_tests/test_sage.py +++ b/tests/unit_tests/test_sage.py @@ -11,10 +11,9 @@ class TestSage(unittest.TestCase): def test_read_sage(self): """Test function for reading sage results and transforming to Prosit format.""" - sage_output_path = Path(__file__).parent / "data" / "sage_output.tsv" expected_sage_internal_path = Path(__file__).parent / "data" / "sage_output_internal.csv" - internal_search_results_df = Sage.read_result(sage_output_path) + internal_search_results_df = Sage(Path(__file__).parent / "data" / "sage_output.tsv").read_result() expected_df = pd.read_csv(expected_sage_internal_path, index_col=0) pd.testing.assert_frame_equal(internal_search_results_df, expected_df)