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@@ -15,5 +15,5 @@ full_name: Mario Picciani | |
| email: [email protected] | ||
| project_name: spectrum_io | ||
| project_short_description: IO related functionalities for oktoberfest. | ||
| version: 0.6.3 | ||
| version: 0.6.4 | ||
| license: MIT | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,237 @@ | ||
| from __future__ import annotations | ||
|
|
||
| import glob | ||
| import logging | ||
| import os | ||
| import re | ||
| from pathlib import Path | ||
| from typing import Dict, Optional, Union | ||
|
|
||
| import numpy as np | ||
| import pandas as pd | ||
| import spectrum_fundamentals.constants as c | ||
| from spectrum_fundamentals.mod_string import xisearch_to_internal | ||
|
|
||
| from .search_results import SearchResults | ||
|
|
||
| logger = logging.getLogger(__name__) | ||
|
|
||
|
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||
| class Scout(SearchResults): | ||
| """Handle search results from xisearch.""" | ||
|
|
||
| def read_result( | ||
| self, | ||
| tmt_label: str = "", | ||
| custom_mods: dict[str, int] | None = None, | ||
| ptm_unimod_id: int | None = 0, | ||
| ptm_sites: list[str] | None = None, | ||
| ) -> pd.DataFrame: | ||
| """ | ||
| Function to read a csv of CSMs and perform some basic formatting. | ||
| :param tmt_label: tmt label as str | ||
| :param custom_mods: dict with custom variable and static identifier and respecitve internal equivalent and mass | ||
| :param ptm_unimod_id: unimod id used for site localization | ||
| :param ptm_sites: possible sites that the ptm can exist on | ||
| :raises NotImplementedError: if TMT label is provided | ||
| :return: pd.DataFrame with the formatted data | ||
| """ | ||
| if tmt_label != "": | ||
| raise NotImplementedError("TMT is not supported for Scout") | ||
|
|
||
| logger.info("Reading search results file...") | ||
| columns_to_read = [ | ||
| "ScanNumber", | ||
| "Charge", | ||
| "ExperimentalMZ", | ||
| "AlphaPeptide", | ||
| "BetaPeptide", | ||
| "AlphaPos", | ||
| "BetaPos", | ||
| "AlphaMappings", | ||
| "BetaMappings", | ||
| "ClassificationScore", | ||
| "Peptide Position 1", | ||
| "Peptide Position 2", | ||
| "Protein 1", | ||
| "Protein 2", | ||
| "FileName", | ||
| ] | ||
|
|
||
| self.results = pd.read_csv(self.path, usecols=columns_to_read) | ||
| logger.info("Finished reading search results file.") | ||
| # Standardize column names | ||
| self.convert_to_internal(mods={}) | ||
| return self.filter_valid_prosit_sequences() | ||
| # df = Scout._filter_duplicates(df) | ||
|
|
||
| @staticmethod | ||
| def _filter_duplicates(df: pd.DataFrame) -> pd.DataFrame: | ||
| """ | ||
| Keep csm with higher score and remove duplicate (only top ranks). | ||
| :param df: df to filter | ||
| :return: filtered df as pd.DataFrame | ||
| """ | ||
| repetitive_combinations = df[df.duplicated(subset=["ScanNumber", "RAW_FILE"], keep=False)] | ||
| filtered_df = repetitive_combinations.groupby(["ScanNumber", "RAW_FILE"]).apply( | ||
| lambda x: x.loc[x["ClassificationScore"].idxmax()] | ||
| ) | ||
| filtered_df.reset_index(drop=True, inplace=True) | ||
| final_df = pd.concat([df.drop_duplicates(subset=["ScanNumber", "RAW_FILE"], keep=False), filtered_df]) | ||
| final_df.reset_index(drop=True, inplace=True) | ||
| df = final_df | ||
| return df | ||
|
|
||
| @staticmethod | ||
| def _extract_modifications(peptide_seq: str): | ||
| modifications = [] | ||
| # Find all matches of modifications | ||
| matches = re.findall(r"([CM])\(\+([\d.]+)\)", peptide_seq) | ||
| for match in matches: | ||
| mod, _ = match | ||
| # Add modification to the list | ||
| if mod == "C": | ||
| modifications.append("cm") | ||
| elif mod == "M": | ||
| modifications.append("ox") | ||
| return ";".join(modifications) | ||
|
|
||
| @staticmethod | ||
| def _extract_modification_positions(peptide_seq: str): | ||
| pattern = r"([A-Z])(\(\+\d+\.\d+\))?" | ||
| matches = re.findall(pattern, peptide_seq) | ||
| split_peptide = [] | ||
| for match in matches: | ||
| amino_acid = match[0] | ||
| modification = match[1] if match[1] else "" | ||
| split_peptide.append(amino_acid + modification) | ||
| positions = [str(i + 1) for i, component in enumerate(split_peptide) if "+" in component] | ||
| return ";".join(positions) | ||
|
|
||
| @staticmethod | ||
| def _self_or_between_mp(df: pd.DataFrame) -> pd.DataFrame: | ||
| df["tmp_id"] = df.index | ||
| df_expl = df.copy() | ||
| df_expl.loc[:, "AlphaMappings"] = df_expl["AlphaMappings"].str.split(";") | ||
| df_expl.loc[:, "BetaMappings"] = df_expl["BetaMappings"].str.split(";") | ||
| df_expl = df_expl.explode("AlphaMappings") | ||
| df_expl = df_expl.explode("BetaMappings") | ||
| df_expl.loc[:, "self"] = False | ||
| df_expl.loc[df_expl["AlphaMappings"] == df_expl["BetaMappings"], "self"] = True | ||
| id_to_self = df_expl.groupby("tmp_id", dropna=False).agg({"self": "max"}).reset_index() | ||
| df = df.drop(["self"], axis=1, errors="ignore").merge(id_to_self, on=["tmp_id"], validate="1:1") | ||
| df.loc[:, "fdr_group"] = df["self"].apply(lambda x: "self" if x else "between") | ||
| return df | ||
|
|
||
| def convert_to_internal( | ||
| self, mods: dict[str, str], ptm_unimod_id: int | None = None, ptm_sites: list[str] | None = None | ||
| ): | ||
| """ | ||
| Convert all columns in the search engine-specific output to the internal format used by Oktoberfest. | ||
| :param mods: dictionary mapping search engine-specific mod patterns (keys) to ProForma standard (values) | ||
| :param ptm_unimod_id: unimod id used for site localization | ||
| :param ptm_sites: possible sites that the ptm can exist on | ||
| """ | ||
| # Filter csms that does not contain any "k" | ||
| df = self.results | ||
| df = df[(df["AlphaPeptide"].str.contains("K")) & (df["BetaPeptide"].str.contains("K"))] | ||
| df["decoy_p1"] = df["AlphaMappings"].str.contains("Reverse").astype(bool) | ||
| df["decoy_p2"] = df["BetaMappings"].str.contains("Reverse").astype(bool) | ||
| df["protein_p1"] = df["AlphaMappings"] | ||
| df["protein_p2"] = df["BetaMappings"] | ||
| df["decoy"] = df["decoy_p1"] | df["decoy_p2"] | ||
| df["REVERSE"] = df["decoy"] | ||
| df["RAW_FILE"] = df["FileName"].apply(lambda x: x.split("\\")[-1]) | ||
| df["MASS"] = df["ExperimentalMZ"] | ||
| df["PRECURSOR_CHARGE"] = df["Charge"] | ||
| df["CROSSLINKER_TYPE"] = "DSSO" | ||
| df["crosslinker_name"] = "DSSO" | ||
| df["linked_aa_p1"] = "K" | ||
| df["linked_aa_p2"] = "K" | ||
| df["linear"] = "False" | ||
| df["match_score"] = "ClassificationScore" | ||
| df["SCORE"] = df["ClassificationScore"] | ||
| df["SCAN_NUMBER"] = df["ScanNumber"] | ||
| df["SEQUENCE_A"] = df["AlphaPeptide"].apply(lambda x: re.sub(r"\([^)]*\)", "", x)) | ||
| df["SEQUENCE_B"] = df["BetaPeptide"].apply(lambda x: re.sub(r"\([^)]*\)", "", x)) | ||
| df["base_sequence_p1"] = df["SEQUENCE_A"] | ||
| df["base_sequence_p2"] = df["SEQUENCE_B"] | ||
| df = df[df.apply(lambda row: row["SEQUENCE_A"][row["AlphaPos"]] == "K", axis=1)] | ||
| df = df[df.apply(lambda row: row["SEQUENCE_B"][row["BetaPos"]] == "K", axis=1)] | ||
| df["Modifications_A"] = df["AlphaPeptide"].apply(Scout._extract_modifications) | ||
| df["Modifications_B"] = df["BetaPeptide"].apply(Scout._extract_modifications) | ||
| df["mods_p1"] = df["AlphaPeptide"].apply(Scout._extract_modifications) | ||
| df["mods_p2"] = df["BetaPeptide"].apply(Scout._extract_modifications) | ||
| df["ModificationPositions1"] = df["AlphaPeptide"].apply(Scout._extract_modification_positions) | ||
| df["ModificationPositions2"] = df["BetaPeptide"].apply(Scout._extract_modification_positions) | ||
| df["CROSSLINKER_POSITION_A"] = df["AlphaPos"] + 1 | ||
| df["CROSSLINKER_POSITION_B"] = df["BetaPos"] + 1 | ||
| df["mod_pos_p1"] = df["AlphaPos"] + 1 | ||
| df["mod_pos_p2"] = df["BetaPos"] + 1 | ||
| df["link_pos_p1"] = df["AlphaPos"] + 1 | ||
| df["link_pos_p2"] = df["BetaPos"] + 1 | ||
| df["PEPTIDE_LENGTH_A"] = df["SEQUENCE_A"].apply(len) | ||
| df["PEPTIDE_LENGTH_B"] = df["SEQUENCE_B"].apply(len) | ||
| df["aa_len_p1"] = df["SEQUENCE_A"].apply(len) | ||
| df["aa_len_p2"] = df["SEQUENCE_B"].apply(len) | ||
| df = Scout._self_or_between_mp(df) | ||
| df["fdr_group"] = np.where( | ||
| df["AlphaMappings"].str.replace("Reverse_", "") == df["BetaMappings"].str.replace("Reverse_", ""), | ||
| "self", | ||
| "between", | ||
| ) | ||
| df.drop(columns=["self"], inplace=True) | ||
| df.drop(columns=["tmp_id"], inplace=True) | ||
| logger.info("Converting Scout peptide sequence to internal format...") | ||
| df["RAW_FILE"] = df["RAW_FILE"].str.replace(".raw", "") | ||
| df["MODIFIED_SEQUENCE_A"] = df.apply( | ||
| lambda row: xisearch_to_internal( | ||
| xl=row["CROSSLINKER_TYPE"], | ||
| seq=row["SEQUENCE_A"], | ||
| mod=row["Modifications_A"], | ||
| crosslinker_position=row["CROSSLINKER_POSITION_A"], | ||
| mod_positions=row["ModificationPositions1"], | ||
| ), | ||
| axis=1, | ||
| result_type="expand", | ||
| ) | ||
| df["MODIFIED_SEQUENCE_B"] = df.apply( | ||
| lambda row: xisearch_to_internal( | ||
| xl=row["CROSSLINKER_TYPE"], | ||
| seq=row["SEQUENCE_B"], | ||
| mod=row["Modifications_B"], | ||
| crosslinker_position=row["CROSSLINKER_POSITION_B"], | ||
| mod_positions=row["ModificationPositions2"], | ||
| ), | ||
| axis=1, | ||
| result_type="expand", | ||
| ) | ||
| new_column_names = { | ||
| "FileName": "run_name", | ||
| "ScanNumber": "scan_number", | ||
| "ExperimentalMZ": "precursor_mass", | ||
| "Charge": "precursor_charge", | ||
| "scan_number": "ScanNumber", | ||
| } | ||
| self.results = df.rename(columns=new_column_names) | ||
|
|
||
| def filter_valid_prosit_sequences(self) -> pd.DataFrame: | ||
| """ | ||
| Filter valid Prosit sequences. | ||
| :return: df after filtration | ||
| """ | ||
| logger.info(f"#sequences before filtering for valid prosit sequences: {len(self.results)}") | ||
| self.results = self.results[(self.results["PEPTIDE_LENGTH_A"] <= 30)] | ||
| self.results = self.results[self.results["PEPTIDE_LENGTH_A"] >= 6] | ||
| self.results = self.results[(self.results["PEPTIDE_LENGTH_B"] <= 30)] | ||
| self.results = self.results[self.results["PEPTIDE_LENGTH_B"] >= 6] | ||
| self.results = self.results[(~self.results["SEQUENCE_A"].str.contains(r"B|\*|\.|U|O|X|Z|\(|\)"))] | ||
| self.results = self.results[(~self.results["SEQUENCE_B"].str.contains(r"B|\*|\.|U|O|X|Z|\(|\)"))] | ||
| self.results = self.results[self.results["PRECURSOR_CHARGE"] <= 6] | ||
| logger.info(f"#sequences after filtering for valid prosit sequences: {len(self.results)}") | ||
|
|
||
| return self.results |
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