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Refer to documentation # for a list of supported languages. diff --git a/poetry.lock b/poetry.lock index 5b7db0c..c0fee78 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,10 +1,9 @@ -# This file is automatically @generated by Poetry and should not be changed by hand. +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. 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"pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [metadata] lock-version = "2.0" -python-versions = "^3.8.0" -content-hash = "87e638b36de0b820e63a88b39366e299f870a668354fef0a5e9ca778da003613" +python-versions = ">=3.8.0,<3.11.0" +content-hash = "896128af4c53517eae6bbbcf64a3af4985139401e37a3212cb6d602e0d323526" diff --git a/pyproject.toml b/pyproject.toml index 127c437..ae2635e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "spectrum_fundamentals" -version = "0.4.2" # <> +version = "0.4.3" # <> description = "Fundamentals public repo" authors = ["WassimG "] license = "MIT" @@ -20,7 +20,7 @@ classifiers = [ [tool.poetry.dependencies] -python = "^3.8.0" +python = ">=3.8.0,<3.11.0" click = ">=8.0.0" rich = ">=10.3.0" PyYAML = ">=5.4.1" diff --git a/spectrum_fundamentals/__init__.py b/spectrum_fundamentals/__init__.py index d31f736..c6f368c 100644 --- a/spectrum_fundamentals/__init__.py +++ b/spectrum_fundamentals/__init__.py @@ -1,7 +1,7 @@ """Initialize fundamentals.""" __author__ = "Victor Giurcoiu" __email__ = "victor.giurcoiu@tum.de" -__version__ = "0.4.2" +__version__ = "0.4.3" import logging import logging.handlers diff --git a/spectrum_fundamentals/__main__.py b/spectrum_fundamentals/__main__.py index b276a03..8ae7658 100644 --- a/spectrum_fundamentals/__main__.py +++ b/spectrum_fundamentals/__main__.py @@ -5,7 +5,7 @@ @click.command() -@click.version_option(version="0.4.2", message=click.style("spectrum_fundamentals Version: 0.4.2")) +@click.version_option(version="0.4.3", message=click.style("spectrum_fundamentals Version: 0.4.3")) def main() -> None: """spectrum_fundamentals.""" diff --git a/spectrum_fundamentals/annotation/annotation.py b/spectrum_fundamentals/annotation/annotation.py index b9b5aab..68e1dcf 100644 --- a/spectrum_fundamentals/annotation/annotation.py +++ b/spectrum_fundamentals/annotation/annotation.py @@ -149,7 +149,6 @@ def annotate_spectra( raw_file_annotations.append(results) results_df = pd.DataFrame(raw_file_annotations) results_df.columns = ["INTENSITIES", "MZ", "CALCULATED_MASS", "removed_peaks"] - logger.info(f"Removed {results_df['removed_peaks'].describe()} redundant peaks") return results_df diff --git a/spectrum_fundamentals/constants.py b/spectrum_fundamentals/constants.py index 712e600..e23207f 100644 --- a/spectrum_fundamentals/constants.py +++ b/spectrum_fundamentals/constants.py @@ -49,7 +49,6 @@ "C[UNIMOD:4]": 2, "K[UNIMOD:737]": 22, "K[UNIMOD:2016]": 22, - "K[UNIMOD:2016]": 22, "K[UNIMOD:214]": 22, "K[UNIMOD:730]": 22, "S[UNIMOD:21]": 25, @@ -73,16 +72,16 @@ "(ox)": "[UNIMOD:35]", "(Oxidation (M))": "[UNIMOD:35]", "(tm)": "[UNIMOD:737]", - "_(tm)": "_[UNIMOD:737]", + "_(tm)": "_[UNIMOD:737]-", "K(tm)": "K[UNIMOD:737]", "(i4)": "[UNIMOD:214]", - "_(i4)": "_[UNIMOD:214]", + "_(i4)": "_[UNIMOD:214]-", "K(i4)": "K[UNIMOD:214]", "(i8)": "[UNIMOD:730]", - "_(i8)": "_[UNIMOD:730]", + "_(i8)": "_[UNIMOD:730]-", "K(i8)": "K[UNIMOD:730]", "(tmp)": "[UNIMOD:2016]", - "_(tmp)": "_[UNIMOD:2016]", + "_(tmp)": "_[UNIMOD:2016]-", "K(tmp)": "K[UNIMOD:2016]", "(ph)": "[UNIMOD:21]", "(Phospho (STY))": "[UNIMOD:21]", diff --git a/spectrum_fundamentals/fragments.py b/spectrum_fundamentals/fragments.py index f8150d1..55fe022 100644 --- a/spectrum_fundamentals/fragments.py +++ b/spectrum_fundamentals/fragments.py @@ -24,10 +24,11 @@ def _get_modifications(peptide_sequence: str) -> Optional[Tuple[Dict[int, float] modification_mass = constants.MOD_MASSES # Handle terminal modifications here for possible_tmt_mod in constants.TMT_MODS.values(): - if peptide_sequence.startswith(possible_tmt_mod): # TMT_6 + n_term_tmt = possible_tmt_mod + "-" + if peptide_sequence.startswith(n_term_tmt): tmt_n_term = 2 modification_deltas.update({0: constants.MOD_MASSES[possible_tmt_mod]}) - peptide_sequence = peptide_sequence[len(possible_tmt_mod) :] + peptide_sequence = peptide_sequence[len(n_term_tmt) :] break if "(" in peptide_sequence: @@ -183,7 +184,7 @@ def initialize_peaks( for ion_type in range(0, number_of_ion_types): # generate all ion types # Check for neutral loss here mass = (ion_type_masses[ion_type] + charge_delta) / charge - min_mass, max_mass = get_min_max_mass(mass_analyzer, mass) + min_mass, max_mass = get_min_max_mass(mass_analyzer, mass, mass_tolerance, unit_mass_tolerance) fragments_meta_data.append( { "ion_type": ion_types[ion_type], # ion type diff --git a/spectrum_fundamentals/metrics/percolator.py b/spectrum_fundamentals/metrics/percolator.py index bd41373..3a71fba 100644 --- a/spectrum_fundamentals/metrics/percolator.py +++ b/spectrum_fundamentals/metrics/percolator.py @@ -1,8 +1,5 @@ import enum -import hashlib import logging -import re -import subprocess from typing import Optional, Tuple, Union import numpy as np @@ -60,7 +57,6 @@ def __init__( all_features_flag: bool = False, regression_method: str = "lowess", fdr_cutoff: float = 0.01, - percolator_version: Optional[float] = 3.05, ): """Initialize a Percolator obj.""" self.metadata = metadata @@ -68,18 +64,8 @@ def __init__( self.all_features_flag = all_features_flag self.regression_method = regression_method self.fdr_cutoff = fdr_cutoff - - self._resolve_percolator_compatibility(percolator_version) super().__init__(pred_intensities, true_intensities, mz) - def _resolve_percolator_compatibility(self, percolator_version: Optional[float] = None): - if percolator_version is None: - result = subprocess.run(["percolator", "-h"], capture_output=True, text=True) - version_line = result.stderr.splitlines()[0].strip() - version = version_line.split("version ")[1] - percolator_version = float(re.sub(r"\.[^.]+$", "", version)) - self.prot_col_name = "Proteins" if percolator_version >= 3.06 else "Protein" - @staticmethod def sample_balanced_over_bins(retention_time_df: pd.DataFrame, sample_size: int = 5000) -> pd.Index: """ @@ -170,18 +156,6 @@ def get_aligned_predicted_retention_times( return aligned_rts_predicted - @staticmethod - def get_scannr(metadata_subset: Union[pd.Series, Tuple[str, int]]) -> int: - """ - Creates a hash of the raw_file and scan number to use as a unique scan number in percolator. - - :param metadata_subset: tuple of (raw_file, scan_number) - :return: hashed unique id - """ - raw_file, scan_number = metadata_subset - s = f"{raw_file}{scan_number}".encode() - return int(hashlib.sha224(s).hexdigest()[:6], 16) - @staticmethod def get_delta_score(scores_df: pd.DataFrame, scoring_feature: str) -> np.ndarray: """ @@ -296,11 +270,11 @@ def add_percolator_metadata_columns(self): spec_id_cols.append("SCAN_EVENT_NUMBER") self.metrics_val["SpecId"] = self.metadata[spec_id_cols].apply(Percolator.get_specid, axis=1) self.metrics_val["Label"] = self.target_decoy_labels - self.metrics_val["ScanNr"] = self.metadata[["RAW_FILE", "SCAN_NUMBER"]].apply(Percolator.get_scannr, axis=1) - + self.metrics_val["ScanNr"] = self.metadata["SCAN_NUMBER"] + self.metrics_val["filename"] = self.metadata["RAW_FILE"] self.metrics_val["Peptide"] = self.metadata["MODIFIED_SEQUENCE"].apply(lambda x: "_." + x + "._") - self.metrics_val[self.prot_col_name] = self.metadata[ + self.metrics_val["Proteins"] = self.metadata[ "MODIFIED_SEQUENCE" ] # we don't need the protein ID to get PSM / peptide results, fill with peptide sequence @@ -397,8 +371,8 @@ def fdrs_to_qvals(fdrs: np.ndarray) -> np.ndarray: def _reorder_columns_for_percolator(self): all_columns = self.metrics_val.columns - first_columns = ["SpecId", "Label", "ScanNr"] - last_columns = ["Peptide", "Protein"] if "Protein" in all_columns else ["Peptide", "Proteins"] + first_columns = ["SpecId", "Label", "ScanNr", "filename"] + last_columns = ["Peptide", "Proteins"] mid_columns = list(set(all_columns) - set(first_columns) - set(last_columns)) new_columns = first_columns + sorted(mid_columns) + last_columns self.metrics_val = self.metrics_val[new_columns] diff --git a/tests/unit_tests/data/perc_output.csv b/tests/unit_tests/data/perc_output.csv index 1309d95..55ed3bb 100644 --- a/tests/unit_tests/data/perc_output.csv +++ b/tests/unit_tests/data/perc_output.csv @@ -1,4 +1,4 @@ -,SpecId,Label,ScanNr,CID,Charge1,Charge2,Charge3,Charge4,Charge5,Charge6,HCD,KR,Mass,RT,UnknownFragmentationMethod,abs_rt_diff,collision_energy_aligned,count_not_observed_and_not_predicted,count_not_observed_and_not_predicted_b,count_not_observed_and_not_predicted_y,count_not_observed_but_predicted,count_not_observed_but_predicted_b,count_not_observed_but_predicted_y,count_observed,count_observed_and_predicted,count_observed_and_predicted_b,count_observed_and_predicted_y,count_observed_b,count_observed_but_not_predicted,count_observed_but_not_predicted_b,count_observed_but_not_predicted_y,count_observed_y,count_predicted,count_predicted_b,count_predicted_y,fraction_not_observed_and_not_predicted,fraction_not_observed_and_not_predicted_b,fraction_not_observed_and_not_predicted_b_vs_predicted_b,fraction_not_observed_and_not_predicted_vs_predicted,fraction_not_observed_and_not_predicted_y,fraction_not_observed_and_not_predicted_y_vs_predicted_y,fraction_not_observed_but_predicted,fraction_not_observed_but_predicted_b,fraction_not_observed_but_predicted_b_vs_predicted,fraction_not_observed_but_predicted_vs_predicted,fraction_not_observed_but_predicted_y,fraction_not_observed_but_predicted_y_vs_predicted,fraction_observed,fraction_observed_and_predicted,fraction_observed_and_predicted_b,fraction_observed_and_predicted_b_vs_predicted_b,fraction_observed_and_predicted_vs_predicted,fraction_observed_and_predicted_y,fraction_observed_and_predicted_y_vs_predicted_y,fraction_observed_b,fraction_observed_but_not_predicted,fraction_observed_but_not_predicted_b,fraction_observed_but_not_predicted_b_vs_predicted_b,fraction_observed_but_not_predicted_vs_predicted,fraction_observed_but_not_predicted_y,fraction_observed_but_not_predicted_y_vs_predicted_y,fraction_observed_y,fraction_predicted,fraction_predicted_b,fraction_predicted_y,iRT,lda_scores,missedCleavages,modified_cosine,pearson_corr,pred_RT,sequence_length,spectral_angle,Peptide,Protein +,SpecId,Label,ScanNr,CID,Charge1,Charge2,Charge3,Charge4,Charge5,Charge6,HCD,KR,Mass,RT,UnknownFragmentationMethod,abs_rt_diff,collision_energy_aligned,count_not_observed_and_not_predicted,count_not_observed_and_not_predicted_b,count_not_observed_and_not_predicted_y,count_not_observed_but_predicted,count_not_observed_but_predicted_b,count_not_observed_but_predicted_y,count_observed,count_observed_and_predicted,count_observed_and_predicted_b,count_observed_and_predicted_y,count_observed_b,count_observed_but_not_predicted,count_observed_but_not_predicted_b,count_observed_but_not_predicted_y,count_observed_y,count_predicted,count_predicted_b,count_predicted_y,fraction_not_observed_and_not_predicted,fraction_not_observed_and_not_predicted_b,fraction_not_observed_and_not_predicted_b_vs_predicted_b,fraction_not_observed_and_not_predicted_vs_predicted,fraction_not_observed_and_not_predicted_y,fraction_not_observed_and_not_predicted_y_vs_predicted_y,fraction_not_observed_but_predicted,fraction_not_observed_but_predicted_b,fraction_not_observed_but_predicted_b_vs_predicted,fraction_not_observed_but_predicted_vs_predicted,fraction_not_observed_but_predicted_y,fraction_not_observed_but_predicted_y_vs_predicted,fraction_observed,fraction_observed_and_predicted,fraction_observed_and_predicted_b,fraction_observed_and_predicted_b_vs_predicted_b,fraction_observed_and_predicted_vs_predicted,fraction_observed_and_predicted_y,fraction_observed_and_predicted_y_vs_predicted_y,fraction_observed_b,fraction_observed_but_not_predicted,fraction_observed_but_not_predicted_b,fraction_observed_but_not_predicted_b_vs_predicted_b,fraction_observed_but_not_predicted_vs_predicted,fraction_observed_but_not_predicted_y,fraction_observed_but_not_predicted_y_vs_predicted_y,fraction_observed_y,fraction_predicted,fraction_predicted_b,fraction_predicted_y,iRT,lda_scores,missedCleavages,modified_cosine,pearson_corr,pred_RT,sequence_length,spectral_angle,Peptide,Proteins 0,20210122_0263_TMUCLHan_Peiru_DDA_IP_C797S_02-7978-AAIGEATRL-2-1,1,10203379,0,0,1,0,0,0,0,1,1,900.50288029264,0.5000000183883155,0,0.0,0.3,2.0,1,1,2.0,1,1,4.0,2.0,1,1,1,2.0,0,2,3,4.0,2,2,0.25,0.3333333333333333,0.5,0.5,0.2,0.5,0.25,0.3333333333333333,0.5,0.5,0.2,0.5,0.5,0.25,0.3333333333333333,0.5,0.5,0.2,0.5,0.3333333333333333,0.25,0.0,0.0,0.5,0.4,1.0,0.6,0.5,0.6666666666666666,0.4,0.5000000183883155,1.7027402478448157e+30,1,0.8959462788501606,0.8831116463997931,0.5000000323590892,9,0.7449534694193893,_.AAIGEATRL._,AAIGEATRL 1,20210122_0263_TMUCLHan_Peiru_DDA_IP_C797S_02-12304-AAVPRAAFL-2-2,-1,5831230,0,0,1,0,0,0,0,1,1,914.53379,1.000000038995633,0,2.533209730870567e-08,0.3,1.0,0,1,1.0,0,1,2.0,1.0,0,1,1,1.0,1,0,1,2.0,0,2,0.25,0.0,0.0,0.5,0.3333333333333333,0.5,0.25,0.0,0.0,0.5,0.3333333333333333,0.5,0.5,0.25,0.0,0.0,0.5,0.3333333333333333,0.5,1.0,0.25,1.0,1.0,0.5,0.0,0.0,0.3333333333333333,0.5,0.0,0.6666666666666666,1.0000000136635356,-1.7027402478448157e+30,1,1.0,0.0,1.5000000246189773,9,0.5903344918223923,_.AAVPRAAFL._,AAVPRAAFL 2,20210122_0263_TMUCLHan_Peiru_DDA_IP_C797S_02-12398-AAYFGVYDTAK-2-3,-1,16059765,0,0,1,0,0,0,0,1,1,1204.5764,1.5000000993570155,0,1.0185683851915428e-07,0.3,1.0,0,1,1.0,0,1,2.0,1.0,0,1,1,1.0,1,0,1,2.0,0,2,0.25,0.0,0.0,0.5,0.3333333333333333,0.5,0.25,0.0,0.0,0.5,0.3333333333333333,0.5,0.5,0.25,0.0,0.0,0.5,0.3333333333333333,0.5,1.0,0.25,1.0,1.0,0.5,0.0,0.0,0.3333333333333333,0.5,0.0,0.6666666666666666,1.499999997500177,-1.7027402478448157e+30,0,1.0,0.0,2.500000056694346,11,0.5903344918223923,_.AAYFGVYDTAK._,AAYFGVYDTAK diff --git a/tests/unit_tests/test_fragments.py b/tests/unit_tests/test_fragments.py index 9cb2993..b0cd7eb 100644 --- a/tests/unit_tests/test_fragments.py +++ b/tests/unit_tests/test_fragments.py @@ -18,11 +18,11 @@ def test_get_modifications_carbamidomethylation(self): def test_get_modifications_tmt_tag(self): """Test get_modifications.""" - assert fragments._get_modifications("[UNIMOD:737]ABC[UNIMOD:4]") == ({0: 229.162932, 2: 57.02146}, 2, "ABC") + assert fragments._get_modifications("[UNIMOD:737]-ABC[UNIMOD:4]") == ({0: 229.162932, 2: 57.02146}, 2, "ABC") def test_get_modifications_tmtpro_tag(self): """Test get_modifications.""" - assert fragments._get_modifications("[UNIMOD:2016]ABC[UNIMOD:4]") == ({0: 304.207146, 2: 57.02146}, 2, "ABC") + assert fragments._get_modifications("[UNIMOD:2016]-ABC[UNIMOD:4]") == ({0: 304.207146, 2: 57.02146}, 2, "ABC") class TestComputeMasses(unittest.TestCase): @@ -63,6 +63,11 @@ def test_compute_peptide_masses(self): seq = "SEQUENC[UNIMOD:4]E" self.assertEqual(fragments.compute_peptide_mass(seq), 1045.2561516699998) + def test_compute_peptide_masses_tmtpro(self): + """Test computation of peptide masses with valid input and tmt tag.""" + seq = "[UNIMOD:737]-SEQUENC[UNIMOD:4]E" + self.assertEqual(fragments.compute_peptide_mass(seq), 1274.41908367) + def test_compute_peptide_masses_with_invalid_syntax(self): """Negative testing of comuptation of peptide mass with unsupported syntax of mod string.""" seq = "SEQUEM(Ox.)CE" diff --git a/tests/unit_tests/test_mod_string.py b/tests/unit_tests/test_mod_string.py index 3f9fcc8..0f4e3b8 100644 --- a/tests/unit_tests/test_mod_string.py +++ b/tests/unit_tests/test_mod_string.py @@ -39,9 +39,9 @@ def test_maxquant_to_internal_variable_dehydration_long(self): def test_maxquant_to_internal_tmt(self): """Test maxquant_to_internal_tmt.""" - fixed_mods = {"C": "C[UNIMOD:4]", "^_": "_[UNIMOD:737]", "K": "K[UNIMOD:737]"} + fixed_mods = {"C": "C[UNIMOD:4]", "^_": "_[UNIMOD:737]-", "K": "K[UNIMOD:737]"} self.assertEqual( - mod.maxquant_to_internal(["_ABCDEFGHK_"], fixed_mods), ["[UNIMOD:737]ABC[UNIMOD:4]DEFGHK[UNIMOD:737]"] + mod.maxquant_to_internal(["_ABCDEFGHK_"], fixed_mods), ["[UNIMOD:737]-ABC[UNIMOD:4]DEFGHK[UNIMOD:737]"] ) def test_maxquant_to_internal_silac(self): @@ -79,13 +79,13 @@ class TestInternalTransformations(unittest.TestCase): def test_internal_without_mods(self): """Test internal with mods to internal without_mods.""" - self.assertEqual(mod.internal_without_mods(["[UNIMOD:737]ABC[UNIMOD:4]DEFGHK[UNIMOD:737]"]), ["ABCDEFGHK"]) + self.assertEqual(mod.internal_without_mods(["[UNIMOD:737]-ABC[UNIMOD:4]DEFGHK[UNIMOD:737]"]), ["ABCDEFGHK"]) def test_internal_to_mod_masses(self): """Test internal with mods to internal without_mods.""" self.assertEqual( - mod.internal_to_mod_mass(["[UNIMOD:737]ABC[UNIMOD:4]DEFGHK[UNIMOD:737]"]), - ["[+229.162932]ABC[+57.02146]DEFGHK[+229.162932]"], + mod.internal_to_mod_mass(["[UNIMOD:737]-ABC[UNIMOD:4]DEFGHK[UNIMOD:737]"]), + ["[+229.162932]-ABC[+57.02146]DEFGHK[+229.162932]"], ) def test_proteomicsdb_to_internal(self): diff --git a/tests/unit_tests/test_percolator.py b/tests/unit_tests/test_percolator.py index 4216c7a..6a2de87 100644 --- a/tests/unit_tests/test_percolator.py +++ b/tests/unit_tests/test_percolator.py @@ -274,12 +274,6 @@ def _create_noisy_logistic_data(): class TestPercolator: """Class to test percolator.""" - def test_get_scannr(self): - """Test get_scannr.""" - np.testing.assert_equal( - perc.Percolator.get_scannr(("20210122_0263_TMUCLHan_Peiru_DDA_IP_C797S_02", 7978)), 10203379 - ) - def test_get_specid(self): """Test get_specid.""" np.testing.assert_string_equal( @@ -361,11 +355,12 @@ def test_calc(self): percolator.metrics_val["SpecId"][0], "20210122_0263_TMUCLHan_Peiru_DDA_IP_C797S_02-7978-AAIGEATRL-2-1" ) np.testing.assert_equal(percolator.metrics_val["Label"][0], 1) - np.testing.assert_equal(percolator.metrics_val["ScanNr"][0], 10203379) + np.testing.assert_equal(percolator.metrics_val["ScanNr"][0], 7978) + np.testing.assert_equal(percolator.metrics_val["filename"][0], "20210122_0263_TMUCLHan_Peiru_DDA_IP_C797S_02") # np.testing.assert_almost_equal(percolator.metrics_val['ExpMass'][0], 900.50345678) np.testing.assert_string_equal(percolator.metrics_val["Peptide"][0], "_.AAIGEATRL._") np.testing.assert_string_equal( - percolator.metrics_val["Protein"][0], "AAIGEATRL" + percolator.metrics_val["Proteins"][0], "AAIGEATRL" ) # we don't need the protein ID to get PSM / peptide results # features