diff --git a/Readme.md b/Readme.md index 276c899..15bd161 100644 --- a/Readme.md +++ b/Readme.md @@ -32,3 +32,69 @@ License 4. Any academic or scholarly publication arising from the use of this Software or any derivative works thereof will include the following acknowledgment: The Software used in this research was created by [INSERT AUTHOR NAMES] of UC Riverside. © 2024 UCR. Commercial entities: please contact mingxun.wang@cs.ucr.edu or tp@ucr.edu for licensing opportunities. + +Useful Utility Functions +------------------------ +ModiFinder includes several useful utility functions for mass spectrometry data analysis and visualization, exposed under `modifinder.utilities`. + +**Reading MGF Files** +You can easily read MGF files into a pandas DataFrame using `read_mgf`. + +```python +from modifinder.utilities import read_mgf + +# Read MGF file +df = read_mgf("path/to/your/spectrum.mgf") +print(df.head()) +``` + +**Visualization** +The `vis` module provides powerful visualization tools. + +```python +from modifinder.utilities import vis +import matplotlib.pyplot as plt + +# Draw a molecule +img = vis.draw_molecule("C1=CC=C(C=C1)O", output_type="png") +plt.imshow(img) +plt.show() + +# Draw a spectrum +# spectrum_data is a list of (mz, intensity) tuples +spectrum_data = df.iloc[0]['spectrum'].T.tolist() +vis.draw_spectrum(spectrum_data) +plt.show() +``` + +Developer Guide +--------------- + +### Running Tests +To run the automated tests, ensure you have `pytest` installed. Then run: + +```bash +pytest +``` +or specifically for utilities: +```bash +pytest modifinder/utilities/tests/ +``` + +### Release Process +ModiFinder uses GitHub Actions for automated releases and documentation deployment. + +**Creating a New Release (PyPI)** +1. Update the version number in `modifinder/__init__.py`, `pyproject.toml` and `docs/source/conf.py`. +2. Create and push a new tag starting with `v` (e.g., `v1.2.3`). + ```bash + git tag v1.2.3 + git push origin v1.2.3 + ``` +3. This triggers the `pypi_publish.yml` workflow, which: + - Builds the package. + - Publishes it to PyPI (and TestPyPI). + - Creates a GitHub Release. + +**Updating Documentation** +Documentation is automatically rebuilt and deployed to GitHub Pages whenever changes are pushed to the `main` branch (via `documentation.yml`). diff --git a/docs/source/conf.py b/docs/source/conf.py index 7eb47fc..68e7b5f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -10,7 +10,7 @@ project = 'ModiFinder' author = 'Reza Shahneh' -release = '1.4' +release = '1.5' # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration diff --git a/docs/source/tutorials/basics.ipynb b/docs/source/tutorials/basics.ipynb index c70f8cf..1547221 100644 --- a/docs/source/tutorials/basics.ipynb +++ b/docs/source/tutorials/basics.ipynb @@ -195,6 +195,56 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Oracle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "to use ModiFinder in the oracle mode, you can just set the is_known variable of your target compound to True and reannotate the network, this way, the annotation engine will use the structure of the unknown to further refine the peak annotations" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "oracle_sample_known = Compound(\"CCMSLIB00011906190\", id=\"known\", **args)\n", + "oracle_sample_modified = Compound(\"CCMSLIB00011906105\", id=\"modified\", **args)\n", + "mf = ModiFinder(oracle_sample_known, oracle_sample_modified, helpers=helpers_array, **args)\n", + "probs = mf.generate_probabilities()\n", + "img_prediction1 = mf.draw_prediction(probs, oracle_sample_known.id, show_legend=True, show_labels=True, shrink_labels=True, size=(1000, 1000), annotation_scale = 0.6)\n", + "mf.network.nodes[oracle_sample_modified.id][\"compound\"].is_known = True\n", + "mf.re_annotate(mf.annotationEngine)\n", + "probs = mf.generate_probabilities()\n", + "img_prediction2 = mf.draw_prediction(probs, oracle_sample_known.id, show_legend=True, show_labels=True, shrink_labels=True, size=(1000, 1000), annotation_scale = 0.6)\n", + "fig, ax = plt.subplots(1, 2, figsize=(20, 10))\n", + "ax[0].imshow(img_prediction1)\n", + "ax[0].set_title('ModiFinder', fontsize=20)\n", + "ax[1].imshow(img_prediction2)\n", + "ax[1].set_title('ModiFinder Oracle', fontsize=20)\n", + "for a in ax:\n", + " a.axis('off')\n", + "plt.show()\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -205,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -288,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -318,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { diff --git a/docs/source/tutorials/basics.md b/docs/source/tutorials/basics.md index 16751d2..0a276f5 100644 --- a/docs/source/tutorials/basics.md +++ b/docs/source/tutorials/basics.md @@ -103,6 +103,38 @@ plt.show() +### Oracle + +to use ModiFinder in the oracle mode, you can just set the is_known variable of your target compound to True and reannotate the network, this way, the annotation engine will use the structure of the unknown to further refine the peak annotations + + +```python +oracle_sample_known = Compound("CCMSLIB00011906190", id="known", **args) +oracle_sample_modified = Compound("CCMSLIB00011906105", id="modified", **args) +mf = ModiFinder(oracle_sample_known, oracle_sample_modified, helpers=helpers_array, **args) +probs = mf.generate_probabilities() +img_prediction1 = mf.draw_prediction(probs, oracle_sample_known.id, show_legend=True, show_labels=True, shrink_labels=True, size=(1000, 1000), annotation_scale = 0.6) +mf.network.nodes[oracle_sample_modified.id]["compound"].is_known = True +mf.re_annotate(mf.annotationEngine) +probs = mf.generate_probabilities() +img_prediction2 = mf.draw_prediction(probs, oracle_sample_known.id, show_legend=True, show_labels=True, shrink_labels=True, size=(1000, 1000), annotation_scale = 0.6) +fig, ax = plt.subplots(1, 2, figsize=(20, 10)) +ax[0].imshow(img_prediction1) +ax[0].set_title('ModiFinder', fontsize=20) +ax[1].imshow(img_prediction2) +ax[1].set_title('ModiFinder Oracle', fontsize=20) +for a in ax: + a.axis('off') +plt.show() + +``` + + + +![png](basics_files/basics_17_0.png) + + + ### Create with your data You can also create your compounds by passing a dictionary @@ -167,7 +199,7 @@ plt.show() -![png](basics_files/basics_16_0.png) +![png](basics_files/basics_19_0.png) diff --git a/docs/source/tutorials/basics_files/basics_17_0.png b/docs/source/tutorials/basics_files/basics_17_0.png new file mode 100644 index 0000000..517fded Binary files /dev/null and b/docs/source/tutorials/basics_files/basics_17_0.png differ diff --git a/docs/source/tutorials/basics_files/basics_19_0.png b/docs/source/tutorials/basics_files/basics_19_0.png new file mode 100644 index 0000000..970ed98 Binary files /dev/null and b/docs/source/tutorials/basics_files/basics_19_0.png differ diff --git a/modifinder/__init__.py b/modifinder/__init__.py index 6b70c59..a60a5b6 100644 --- a/modifinder/__init__.py +++ b/modifinder/__init__.py @@ -5,16 +5,46 @@ ModiFinder is a Python package for the identification of modifications in mass spectrometry data. """ -from modifinder import convert -from modifinder.convert import * +__version__ = "1.5.0" -from modifinder.exceptions import * +import modifinder.convert as convert +from modifinder.classes import Compound, Spectrum, ModiFinder, EdgeDetail, MatchType, StructureMeta +from modifinder.convert import ( + to_compound, + to_spectrum, + compound_to_dict, + spectrum_to_dict, +) +from modifinder.exceptions import ( + ModiFinderError, + ModiFinderException, + ModiFinderNetworkError, + ModiFinderNotImplementedError, + ModiFinderNotSolvableError, +) +from modifinder.engines.alignment.CosineAlignmentEngine import CosineAlignmentEngine +from modifinder.engines.annotation.MAGMaAnnotationEngine import MAGMaAnnotationEngine +from modifinder.engines.evaluation.BasicEvaluationEngine import BasicEvaluationEngine -from modifinder import classes -from modifinder.classes import * - -from modifinder import utilities -from modifinder.utilities import * - -from modifinder import engines -from modifinder.engines import * \ No newline at end of file +__all__ = [ + "__version__", + "Compound", + "Spectrum", + "ModiFinder", + "EdgeDetail", + "MatchType", + "StructureMeta", + "convert", + "to_compound", + "to_spectrum", + "compound_to_dict", + "spectrum_to_dict", + "ModiFinderException", + "ModiFinderError", + "ModiFinderNetworkError", + "ModiFinderNotImplementedError", + "ModiFinderNotSolvableError", + "CosineAlignmentEngine", + "MAGMaAnnotationEngine", + "BasicEvaluationEngine", +] diff --git a/modifinder/classes/Compound.py b/modifinder/classes/Compound.py index 0465a00..e2ad5a1 100644 --- a/modifinder/classes/Compound.py +++ b/modifinder/classes/Compound.py @@ -11,7 +11,7 @@ # import modifinder as mf from modifinder.classes.Spectrum import Spectrum from modifinder.classes.StructureMeta import StructureMeta -from modifinder import convert +from .. import convert class Compound: diff --git a/modifinder/classes/ModiFinder.py b/modifinder/classes/ModiFinder.py index eb288d2..38b5080 100644 --- a/modifinder/classes/ModiFinder.py +++ b/modifinder/classes/ModiFinder.py @@ -6,7 +6,7 @@ This class Builds and maintains a network of compounds where the nodes are compounds (known and unknown) """ -from modifinder import convert as convert +from .. import convert as convert from modifinder.classes.Compound import Compound from modifinder.classes.EdgeDetail import EdgeDetail, MatchType from modifinder.engines import AlignmentEngine, AnnotationEngine diff --git a/modifinder/classes/Spectrum.py b/modifinder/classes/Spectrum.py index 0297662..ac07ece 100644 --- a/modifinder/classes/Spectrum.py +++ b/modifinder/classes/Spectrum.py @@ -1,7 +1,7 @@ import json from modifinder.utilities.gnps_types import adduct_mapping import modifinder.utilities.general_utils as general_utils -from modifinder import convert +from .. import convert import numpy as np import bisect import uuid @@ -162,6 +162,12 @@ def update(self, peaks = None, peaks_json = None, mz=None, intensity=None, precu if self.spectrum_id is None: self.spectrum_id = str(uuid.uuid4()) + # make sure types are correct + if self.precursor_mz is not None: + self.precursor_mz = float(self.precursor_mz) + if self.precursor_charge is not None: + self.precursor_charge = int(self.precursor_charge) + def __str__(self): object_dict = self.__dict__ @@ -356,4 +362,3 @@ def get_peak_indexes(self, mz, mz_tolerance = 0.02, ppm_tolerance = 40.0, **kwar return list(range(left_index, right_index)) - diff --git a/modifinder/convert.py b/modifinder/convert.py index f3358de..2aa0b1b 100644 --- a/modifinder/convert.py +++ b/modifinder/convert.py @@ -1,8 +1,16 @@ -import modifinder as mf import modifinder.utilities.network as network from modifinder.utilities.general_utils import parse_data_to_universal +from modifinder.exceptions import ModiFinderError from copy import deepcopy +def _get_compound_class(): + from modifinder.classes.Compound import Compound + return Compound + +def _get_spectrum_class(): + from modifinder.classes.Spectrum import Spectrum + return Spectrum + def to_compound(data = None, use_object=None, **kwargs): """Make a Compound object from the data @@ -24,16 +32,17 @@ def to_compound(data = None, use_object=None, **kwargs): if data is None: data = parse_data_to_universal(kwargs) try: + Compound = _get_compound_class() if use_object: compound = use_object compound.clear() compound.update(**data) else: - compound = mf.Compound() + compound = Compound() compound.update(**data) return compound except Exception as err: - raise mf.ModiFinderError("Input data is not a valid dictionary. " + str(err)) from err + raise ModiFinderError("Input data is not a valid dictionary. " + str(err)) from err # Compound Object if hasattr(data, "spectrum"): @@ -51,7 +60,7 @@ def to_compound(data = None, use_object=None, **kwargs): return compound except Exception as err: - raise mf.ModiFinderError("Input data is not a valid Compound object. " + str(err)) from err + raise ModiFinderError("Input data is not a valid Compound object. " + str(err)) from err # USI if isinstance(data, str): @@ -59,32 +68,34 @@ def to_compound(data = None, use_object=None, **kwargs): data = network.get_data(data) data.update(kwargs) + Compound = _get_compound_class() if use_object: compound = use_object compound.clear() compound.update(**data) else: - compound = mf.Compound() + compound = Compound() compound.update(**data) return compound except Exception as err: - raise mf.ModiFinderError("Input data is not a valid USI string. " + str(err)) from err + raise ModiFinderError("Input data is not a valid USI string. " + str(err)) from err # Dictionary if isinstance(data, dict): data = parse_data_to_universal(data) data.update(kwargs) try: + Compound = _get_compound_class() if use_object: compound = use_object compound.clear() compound.update(**data) else: - compound = mf.Compound(**data) + compound = Compound(**data) return compound except Exception as err: - raise mf.ModiFinderError("Input data is not a valid dictionary. " + str(err)) from err + raise ModiFinderError("Input data is not a valid dictionary. " + str(err)) from err def compound_to_dict(compound): @@ -119,15 +130,16 @@ def to_spectrum(data = None, use_object=None, needs_parse = True, **kwargs): else: data = kwargs try: + Spectrum = _get_spectrum_class() if use_object: spectrum = use_object spectrum.clear() spectrum.update(**data) else: - spectrum = mf.Spectrum(incoming_data=data) + spectrum = Spectrum(incoming_data=data) return spectrum except Exception as err: - raise mf.ModiFinderError("Input data is not a valid dictionary. " + str(err)) from err + raise ModiFinderError("Input data is not a valid dictionary. " + str(err)) from err # Spectrum Object if hasattr(data, "mz"): @@ -147,7 +159,7 @@ def to_spectrum(data = None, use_object=None, needs_parse = True, **kwargs): return spectrum except Exception as err: - raise mf.ModiFinderError("Input data is not a valid Spectrum object. " + str(err)) from err + raise ModiFinderError("Input data is not a valid Spectrum object. " + str(err)) from err # USI if isinstance(data, str): @@ -156,16 +168,17 @@ def to_spectrum(data = None, use_object=None, needs_parse = True, **kwargs): data = parse_data_to_universal(data) data.update(kwargs) data = parse_data_to_universal(data) + Spectrum = _get_spectrum_class() if use_object: spectrum = use_object spectrum.clear() spectrum.update(**data) else: - spectrum = mf.Spectrum(**data) + spectrum = Spectrum(**data) return spectrum except Exception as err: - raise mf.ModiFinderError("Input data is not a valid USI string. " + str(err)) from err + raise ModiFinderError("Input data is not a valid USI string. " + str(err)) from err # Dictionary if isinstance(data, dict): @@ -174,32 +187,34 @@ def to_spectrum(data = None, use_object=None, needs_parse = True, **kwargs): try: # add kwargs to data data.update(kwargs) + Spectrum = _get_spectrum_class() if use_object: spectrum = use_object spectrum.clear() spectrum.update(**data) else: - spectrum = mf.Spectrum(**data) + spectrum = Spectrum(**data) return spectrum except Exception as err: - raise mf.ModiFinderError("Input data is not a valid dictionary. " + str(err)) from err + raise ModiFinderError("Input data is not a valid dictionary. " + str(err)) from err if isinstance(data, list): new_data = dict() new_data["mz"] = [x[0] for x in data] new_data["intensity"] = [x[1] for x in data] try: + Spectrum = _get_spectrum_class() if use_object: spectrum = use_object spectrum.clear() spectrum.update(**new_data) else: - spectrum = mf.Spectrum(**new_data) + spectrum = Spectrum(**new_data) return spectrum except Exception as err: - raise mf.ModiFinderError("Input data is not a valid list. " + str(err)) from err + raise ModiFinderError("Input data is not a valid list. " + str(err)) from err - raise mf.ModiFinderError("Input data is not a valid object.") + raise ModiFinderError("Input data is not a valid object.") @@ -209,4 +224,4 @@ def spectrum_to_dict(spectrum): spectrum_dict = spectrum.__dict__ # make sure nothing is passed by reference spectrum_dict = deepcopy(spectrum_dict) - return spectrum_dict \ No newline at end of file + return spectrum_dict diff --git a/modifinder/runner.py b/modifinder/runner.py index 2af1794..429270d 100644 --- a/modifinder/runner.py +++ b/modifinder/runner.py @@ -2,7 +2,8 @@ This Module is to run the modifinder algorithm on the give input data. """ -from modifinder import ModiFinder, Compound, BasicEvaluationEngine +from modifinder import ModiFinder, Compound +from modifinder.engines.evaluation.BasicEvaluationEngine import BasicEvaluationEngine from modifinder.utilities import visualizer as mf_vis from modifinder.utilities.general_utils import entropy import matplotlib.pyplot as plt diff --git a/modifinder/utilities/__init__.py b/modifinder/utilities/__init__.py index e69de29..e28dc50 100644 --- a/modifinder/utilities/__init__.py +++ b/modifinder/utilities/__init__.py @@ -0,0 +1,3 @@ +from . import visualizer as vis +from . import general_utils as gu +from .general_utils import read_mgf, write_mgf diff --git a/modifinder/utilities/general_utils.py b/modifinder/utilities/general_utils.py index 751b6ef..f7a1dbe 100644 --- a/modifinder/utilities/general_utils.py +++ b/modifinder/utilities/general_utils.py @@ -43,17 +43,38 @@ def is_shifted(val1:float, val2:float, ppm_tolerance:float=None, mz_tolerance:fl def read_mgf(mgf_path: str) -> pd.DataFrame: """ - Read an MGF file into a pandas DataFrame - + Read an MGF file into a pandas DataFrame. + + This function reads a Mass Spectrometry data file in MGF format and returns it as a pandas DataFrame. + Each row in the DataFrame represents a spectrum, with metadata as columns and the spectrum data + (m/z and intensity arrays) stored in the 'spectrum' column. + Parameters ---------- - mgf_path : str - The path to the MGF file to be read - + mgf_path : str + The file path to the MGF file to be read. + Returns ------- - pd.DataFrame - pandas DataFrame with columns as metadata and 'spectrum' as the m/z and intensity values + pd.DataFrame + A pandas DataFrame where each row corresponds to a spectrum. + Common columns include: + - 'spectrum': A numpy array of shape (2, N) containing m/z (row 0) and intensity (row 1) values. + - 'precursor_mz': The m/z of the precursor ion. + - 'charge': The charge state of the precursor ion (if available). + - 'scans': The scan number (if available). + - Any other metadata fields present in the MGF file. + + Example + ------- + .. code-block:: python + + import modifinder.utilities.general_utils as gu + df = gu.read_mgf("path/to/file.mgf") + print(df.head()) + # Access the first spectrum's m/z and intensity + mz_array = df.iloc[0]['spectrum'][0] + intensity_array = df.iloc[0]['spectrum'][1] """ msms_df = [] @@ -321,9 +342,9 @@ def parse_data_to_universal(data): res[converted_key] = gt.adduct_mapping.get(value, value) else: try: - if key in ["precursor_charge", "precursor_charge", "ms_level", "scan", "exact_mass"]: + if converted_key in ["precursor_charge", "precursor_mz", "ms_level", "scan", "exact_mass"]: value = float(value) - if key in ["precursor_charge", "charge", "ms_level"]: + if converted_key in ["precursor_charge", "charge", "ms_level"]: value = int(value) except Exception: raise ValueError(f"Could not convert {key} to number") diff --git a/modifinder/utilities/gnps_types.py b/modifinder/utilities/gnps_types.py index 448c4fe..95e04a4 100644 --- a/modifinder/utilities/gnps_types.py +++ b/modifinder/utilities/gnps_types.py @@ -12,6 +12,45 @@ ) +def convert_to_SpectrumTuple(peaks, precursor_mz, precursor_charge): + if peaks is None or len(peaks) == 0: + return None + if not all(isinstance(peak, (list, tuple)) and len(peak) == 2 for peak in peaks): + raise ValueError("Peaks must be a list of (mz, intensity) pairs") + mz = [peak[0] for peak in peaks] + intensity = [peak[1] for peak in peaks] + return SpectrumTuple( + precursor_mz=float(precursor_mz), + precursor_charge=int(precursor_charge), + mz=mz, + intensity=intensity, + ) + + +def convert_to_SpectrumTuple_seprated(mz, intensity, precursor_mz, precursor_charge): + if mz is None or intensity is None or len(mz) == 0: + return None + if len(mz) != len(intensity): + raise ValueError("mz and intensity must have the same length") + return SpectrumTuple( + precursor_mz=float(precursor_mz), + precursor_charge=int(precursor_charge), + mz=list(mz), + intensity=list(intensity), + ) + + +def Convert_SpectrumTuple_to_peaks(spectrum): + return list(zip(spectrum.mz, spectrum.intensity)) + + +def convert_to_universal_key(key: str) -> str: + key = key.lower() + key = key.replace(" ", "_") + return gnps_keys_mapping.get(key, key) + + + adduct_mapping = {'M+H': '[M+H]+', '[M+H]': '[M+H]+', '[M+H]+': '[M+H]+', @@ -182,4 +221,4 @@ ## intensity "fragment_intensities": "intensity", "intensities": "intensity", -} \ No newline at end of file +} diff --git a/modifinder/utilities/spectrum_utils.py b/modifinder/utilities/spectrum_utils.py index 7817de1..bfc05c3 100644 --- a/modifinder/utilities/spectrum_utils.py +++ b/modifinder/utilities/spectrum_utils.py @@ -1,5 +1,5 @@ from modifinder.utilities import general_utils as mf_gu -from modifinder import Spectrum +from modifinder.classes.Spectrum import Spectrum import tqdm def _cluster_spectrums(spectrums, ppm_tolerance = 10, mz_tolerance = 0.1, verbose = False): @@ -52,7 +52,7 @@ def _cluster_spectrums(spectrums, ppm_tolerance = 10, mz_tolerance = 0.1, verbos return clusters -def aggregate_spectrums(spectrums, ppm_tolerance = 10, mz_tolerance = 0.1, consensus_majority_ratio = 0, **kwargs): +def aggregate_spectrums(spectrums, ppm_tolerance = 10, mz_tolerance = 0.1, consensus_majority_ratio = 1, **kwargs): ''' This function is to create a consensus spectrum from a list of spectrums of the same compound all the other components of the generated spectrum such as the charge, ... are taken from the first spectrum. @@ -212,4 +212,4 @@ def add_adduct(spectrum, adduct): result.precursor_mz += adduct_mass result.adduct = adduct - return result \ No newline at end of file + return result diff --git a/modifinder/utilities/tests/test_gnps_types.py b/modifinder/utilities/tests/test_gnps_types.py index 0a5e8fb..0e2f407 100644 --- a/modifinder/utilities/tests/test_gnps_types.py +++ b/modifinder/utilities/tests/test_gnps_types.py @@ -42,7 +42,7 @@ def test_convert_to_universal_key(self): self.assertEqual(convert_to_universal_key("precursor_mz"), "precursor_mz") self.assertEqual(convert_to_universal_key("smiles"), "smiles") self.assertEqual(convert_to_universal_key("SMILES"), "smiles") - self.assertEqual(convert_to_universal_key("charge"), "charge") + self.assertEqual(convert_to_universal_key("charge"), "precursor_charge") self.assertEqual(convert_to_universal_key("adduct"), "adduct") self.assertEqual(convert_to_universal_key("unknown_key"), "unknown_key") @@ -54,8 +54,8 @@ def test_parse_data_to_universal(self): } expected = { "peaks": [{"mz": 100.0, "intensity": 200.0}, {"mz": 150.0, "intensity": 300.0}], - "Precursor_MZ": 500.0, - "Charge": 2 + "precursor_mz": 500.0, + "precursor_charge": 2 } result = parse_data_to_universal(data) self.assertEqual(result, expected) @@ -67,4 +67,4 @@ def test_Convert_SpectrumTuple_to_peaks(self): self.assertEqual(result, expected) if __name__ == '__main__': - unittest.main() \ No newline at end of file + unittest.main() diff --git a/pyproject.toml b/pyproject.toml index 2e06615..c5c239c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "modifinder" -version = "1.4.4-beta" +version = "1.5.0" authors = [ { name = "Reza Shahneh", email = "mzare008@ucr.edu" }, ] diff --git a/tests/test_files/pair.mgf b/tests/test_files/pair.mgf new file mode 100644 index 0000000..4eaff47 --- /dev/null +++ b/tests/test_files/pair.mgf @@ -0,0 +1,46 @@ +BEGIN IONS +PEPMASS=62.0600 +CHARGE=1 +MSLEVEL=2 +SOURCE_INSTRUMENT=ESI-Orbitrap +IONMODE=Positive +ORGANISM=test1 +NAME=test1 +PI=Reza +DATACOLLECTOR=Reza +SMILES=ONCC +INCHI="InChI=1S/C2H7NO/c1-2-3-4/h3-4H,2H2,1H3" +INCHIAUX=N/A +PUBMED=na +SUBMITUSER=Reza +LIBRARYQUALITY=1 +SPECTRUMID=REZA1 +SCANS=1 +32.0495 20 +34.0287 30 +62.0600 50 +END IONS + + +BEGIN IONS +PEPMASS=76.0757 +CHARGE=1 +MSLEVEL=2 +SOURCE_INSTRUMENT=ESI-Orbitrap +IONMODE=Positive +ORGANISM=test2 +NAME=test2 +PI=Reza +DATACOLLECTOR=Reza +SMILES=ON(C)CC +INCHI="InChI=1S/C3H9NO/c1-3-4(2)5/h5H,3H2,1-2H3" +INCHIAUX=N/A +PUBMED=na +SUBMITUSER=Reza +LIBRARYQUALITY=1 +SPECTRUMID=REZA2 +SCANS=1 +46.0651 35 +48.0444 10 +76.0757 55 +END IONS diff --git a/tests/test_pair_mgf_flow.py b/tests/test_pair_mgf_flow.py new file mode 100644 index 0000000..d2b0294 --- /dev/null +++ b/tests/test_pair_mgf_flow.py @@ -0,0 +1,119 @@ +import unittest +from pathlib import Path + +import numpy as np + +from modifinder import Compound, ModiFinder, Spectrum +from modifinder.utilities.general_utils import read_mgf +from modifinder.utilities import mol_utils as mf_mu +from modifinder.utilities import visualizer as mf_vis + + +class TestPairMGFFlow(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.mgf_path = Path(__file__).resolve().parent / "test_files" / "pair.mgf" + cls.mgf_df = read_mgf(str(cls.mgf_path)) + + @staticmethod + def _coerce_charge(charge): + if isinstance(charge, (list, tuple)): + return int(charge[0]) if charge else None + if charge is None: + return None + return int(charge) + + @classmethod + def _compound_from_row(cls, row): + row = row.to_dict() + charge = cls._coerce_charge(row.get("charge")) + spectrum = row["spectrum"] + data = { + "mz": spectrum[0], + "intensity": spectrum[1], + "precursor_mz": row["precursor_mz"], + "precursor_charge": charge, + "smiles": row["smiles"], + "name": row.get("name"), + "spectrum_id": row.get("spectrumid"), + } + return Compound(data) + + def test_read_mgf(self): + self.assertEqual(len(self.mgf_df), 2) + self.assertIn("spectrum", self.mgf_df.columns) + self.assertIn("smiles", self.mgf_df.columns) + self.assertEqual(self.mgf_df.iloc[0]["spectrum"].shape[0], 2) + + def test_create_spectrum(self): + row = self.mgf_df.iloc[0].to_dict() + charge = self._coerce_charge(row.get("charge")) + spectrum = Spectrum( + mz=row["spectrum"][0], + intensity=row["spectrum"][1], + precursor_mz=row["precursor_mz"], + precursor_charge=charge, + ) + self.assertIsNotNone(spectrum.mz) + self.assertEqual(len(spectrum.mz), 3) + self.assertEqual(spectrum.precursor_charge, 1) + + def test_create_compound(self): + compound = self._compound_from_row(self.mgf_df.iloc[0]) + self.assertIsNotNone(compound.structure) + self.assertIsNotNone(compound.spectrum) + symbols = {atom.GetSymbol() for atom in compound.structure.GetAtoms()} + self.assertIn("N", symbols) + + def test_modifinder_solve_and_modification_site(self): + known = self._compound_from_row(self.mgf_df.iloc[0]) + unknown = self._compound_from_row(self.mgf_df.iloc[1]) + mf = ModiFinder(known, unknown, ppm_tolerance=40) + self.assertIsNone(mf.solve(unknown.id)) + mod_sites = mf_mu.get_modification_nodes(known.structure, unknown.structure, True) + self.assertEqual(len(mod_sites), 1) + n_indices = [atom.GetIdx() for atom in known.structure.GetAtoms() if atom.GetSymbol() == "N"] + self.assertEqual(mod_sites[0], n_indices[0]) + + def test_draw_spectrums(self): + known = self._compound_from_row(self.mgf_df.iloc[0]) + unknown = self._compound_from_row(self.mgf_df.iloc[1]) + img_known = mf_vis.draw_spectrum(known.spectrum) + img_unknown = mf_vis.draw_spectrum(unknown.spectrum) + self.assertTrue(hasattr(img_known, "shape")) + self.assertTrue(hasattr(img_unknown, "shape")) + self.assertGreater(img_known.size, 0) + self.assertGreater(img_unknown.size, 0) + + def test_draw_molecules(self): + known = self._compound_from_row(self.mgf_df.iloc[0]) + unknown = self._compound_from_row(self.mgf_df.iloc[1]) + img_known = mf_vis.draw_molecule(known.structure) + img_unknown = mf_vis.draw_molecule(unknown.structure) + self.assertTrue(hasattr(img_known, "shape")) + self.assertTrue(hasattr(img_unknown, "shape")) + self.assertGreater(img_known.size, 0) + self.assertGreater(img_unknown.size, 0) + + def test_draw_alignment(self): + known = self._compound_from_row(self.mgf_df.iloc[0]) + unknown = self._compound_from_row(self.mgf_df.iloc[1]) + mf = ModiFinder(known, unknown, ppm_tolerance=40) + img = mf.draw_alignment(known.id, unknown.id) + self.assertTrue(hasattr(img, "shape")) + self.assertGreater(img.size, 0) + + def test_draw_heatmap(self): + known = self._compound_from_row(self.mgf_df.iloc[0]) + unknown = self._compound_from_row(self.mgf_df.iloc[1]) + mf = ModiFinder(known, unknown, ppm_tolerance=40) + n_indices = [atom.GetIdx() for atom in known.structure.GetAtoms() if atom.GetSymbol() == "N"] + scores = np.zeros(known.structure.GetNumAtoms()) + scores[n_indices[0]] = 1.0 + img = mf.draw_prediction(scores, known.id, show_legend=False) + self.assertTrue(hasattr(img, "shape")) + self.assertGreater(img.size, 0) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_utilities/test_general_utils.py b/tests/test_utilities/test_general_utils.py index e3c4755..6c64e66 100644 --- a/tests/test_utilities/test_general_utils.py +++ b/tests/test_utilities/test_general_utils.py @@ -1,23 +1,37 @@ import unittest -from modifinder.utilities.general_utils import is_shifted +from modifinder.utilities.general_utils import is_shifted, parse_data_to_universal class TestIsShifted(unittest.TestCase): def test_is_shifted_with_ppm(self): - self.assertTrue(is_shifted(100.0, 100.1, ppm=500)) - self.assertFalse(is_shifted(100.0, 100.0001, ppm=500)) + self.assertTrue(is_shifted(100.0, 100.1, ppm_tolerance=500)) + self.assertFalse(is_shifted(100.0, 100.0001, ppm_tolerance=500)) def test_is_shifted_with_mz_tol(self): - self.assertTrue(is_shifted(100.0, 100.2, mz_tol=0.1)) - self.assertFalse(is_shifted(100.0, 100.05, mz_tol=0.1)) + self.assertTrue(is_shifted(100.0, 100.2, mz_tolerance=0.1)) + self.assertFalse(is_shifted(100.0, 100.05, mz_tolerance=0.1)) def test_is_shifted_with_both_ppm_and_mz_tol(self): - self.assertTrue(is_shifted(100.0, 100.2, ppm=500, mz_tol=0.1)) - self.assertTrue(is_shifted(100.0, 100.0002, ppm=1, mz_tol=0.0001)) - self.assertFalse(is_shifted(100.0, 100.00005, ppm=1, mz_tol=0.0001)) + self.assertTrue(is_shifted(100.0, 100.2, ppm_tolerance=500, mz_tolerance=0.1)) + self.assertTrue(is_shifted(100.0, 100.0002, ppm_tolerance=1, mz_tolerance=0.0001)) + self.assertFalse(is_shifted(100.0, 100.00005, ppm_tolerance=1, mz_tolerance=0.0001)) def test_is_shifted_without_ppm_or_mz_tol(self): with self.assertRaises(ValueError): is_shifted(100.0, 100.1) + + def test_parse_data_to_universal(self): + data = { + "peaks_json": '[{"mz": 100.0, "intensity": 200.0}, {"mz": 150.0, "intensity": 300.0}]', + "precursor_mz": "500.0", + "Charge": "2" + } + expected = { + "peaks": [{"mz": 100.0, "intensity": 200.0}, {"mz": 150.0, "intensity": 300.0}], + "precursor_mz": 500.0, + "precursor_charge": 2 + } + result = parse_data_to_universal(data) + self.assertEqual(result, expected) if __name__ == '__main__': - unittest.main() \ No newline at end of file + unittest.main() diff --git a/tests/test_utilities/test_gnps_types.py b/tests/test_utilities/test_gnps_types.py index 6bb548d..c1a6ce8 100644 --- a/tests/test_utilities/test_gnps_types.py +++ b/tests/test_utilities/test_gnps_types.py @@ -4,7 +4,6 @@ convert_to_SpectrumTuple, convert_to_SpectrumTuple_seprated, convert_to_universal_key, - parse_data_to_universal, Convert_SpectrumTuple_to_peaks, SpectrumTuple ) @@ -39,27 +38,13 @@ def test_convert_to_SpectrumTuple_seprated(self): self.assertIsNone(convert_to_SpectrumTuple_seprated([], [], precursor_mz, precursor_charge)) def test_convert_to_universal_key(self): - self.assertEqual(convert_to_universal_key("precursor_mz"), "Precursor_MZ") + self.assertEqual(convert_to_universal_key("precursor_mz"), "precursor_mz") self.assertEqual(convert_to_universal_key("smiles"), "smiles") self.assertEqual(convert_to_universal_key("SMILES"), "smiles") - self.assertEqual(convert_to_universal_key("charge"), "Charge") - self.assertEqual(convert_to_universal_key("adduct"), "Adduct") + self.assertEqual(convert_to_universal_key("charge"), "precursor_charge") + self.assertEqual(convert_to_universal_key("adduct"), "adduct") self.assertEqual(convert_to_universal_key("unknown_key"), "unknown_key") - def test_parse_data_to_universal(self): - data = { - "peaks_json": '[{"mz": 100.0, "intensity": 200.0}, {"mz": 150.0, "intensity": 300.0}]', - "precursor_mz": "500.0", - "Charge": "2" - } - expected = { - "peaks": [{"mz": 100.0, "intensity": 200.0}, {"mz": 150.0, "intensity": 300.0}], - "Precursor_MZ": 500.0, - "Charge": 2 - } - result = parse_data_to_universal(data) - self.assertEqual(result, expected) - def test_Convert_SpectrumTuple_to_peaks(self): spectrum = SpectrumTuple(precursor_mz=500.0, precursor_charge=2, mz=[100.0, 150.0], intensity=[200.0, 300.0]) expected = [(100.0, 200.0), (150.0, 300.0)] @@ -67,4 +52,4 @@ def test_Convert_SpectrumTuple_to_peaks(self): self.assertEqual(result, expected) if __name__ == '__main__': - unittest.main() \ No newline at end of file + unittest.main()