-
Notifications
You must be signed in to change notification settings - Fork 11
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge branch 'main' into mean_median_plot
- Loading branch information
Showing
52 changed files
with
957 additions
and
233 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -21,4 +21,4 @@ | |
| "flake8.args": [ | ||
| "--max-line-length=120", | ||
| ], | ||
| } | ||
| } | ||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,122 @@ | ||
| """MSAngel creates modular pipelines that allows several search engines to identify | ||
| peptides, which are then quantified with Proline. | ||
| The parameters are provided in a .json file. | ||
| MSAngel allows for multiple search engines to be used in the same pipeline. So it | ||
| requires a list of search engines and their respective parameters, which are then | ||
| concatenated. | ||
| Relevant information in file: | ||
| """ | ||
|
|
||
| import json | ||
| import pathlib | ||
| from typing import Union | ||
|
|
||
| import pandas as pd | ||
|
|
||
| from proteobench.io.params import ProteoBenchParameters | ||
|
|
||
|
|
||
| def extract_search_engine(search_params: list) -> dict: | ||
| """ | ||
| Extract search engine parameters from the JSON data. | ||
| The parameter format depends on the search engine used, so this functino needs to be | ||
| updated for each search engine. Currently, it is set up for: | ||
| . Mascot | ||
| """ | ||
|
|
||
| all_search_engines = [] | ||
| for each_search_params in search_params["operations"]: | ||
| print("1") | ||
| if "searchEnginesWithForms" in each_search_params: | ||
| all_search_engines.append(each_search_params["searchEnginesWithForms"][0][0]) | ||
|
|
||
| return all_search_engines | ||
|
|
||
|
|
||
| def extract_params(fname: Union[str, pathlib.Path]) -> ProteoBenchParameters: | ||
| """ | ||
| Parse MSAangel quantification tool JSON parameter file and extract relevant parameters. | ||
| Args: | ||
| fname (str or pathlib.Path): The path to the Sage JSON parameter file. | ||
| Returns: | ||
| ProteoBenchParameters: The extracted parameters as a `ProteoBenchParameters` object. | ||
| """ | ||
| params = ProteoBenchParameters() | ||
|
|
||
| try: | ||
| # If the input is a file-like object (e.g., StringIO), decode it | ||
| file_contents = fname.getvalue().decode("utf-8") | ||
| data = json.loads(file_contents) | ||
| except AttributeError: | ||
| # Otherwise, treat it as a file path | ||
| with open(fname, "r") as file_contents: | ||
| data = json.load(file_contents) | ||
|
|
||
| # Extract parameters from the JSON data | ||
| params.software_name = "MSAngel" | ||
| params.software_version = data["msAngelVersion"] | ||
|
|
||
| ## Extract the search engine(s) parameters before concatenating them: | ||
| all_search_engines = extract_search_engine(data) | ||
| params.search_engines = all_search_engines.join(",") | ||
| all_search_engines = [] | ||
| all_enzyme = [] | ||
| all_allowed_miscleavages = [] | ||
| all_fixed_mods = [] | ||
| all_variable_mods = [] | ||
|
|
||
| # TODO needs to have actual values | ||
| all_search_params = {} | ||
|
|
||
| for key, value in all_search_params.items(): | ||
| all_search_engines.append(value["format"]) | ||
| all_enzyme.append(value["enzyme"]["cleave_at"]) | ||
| all_allowed_miscleavages.append(value["enzyme"]["missed_cleavages"]) | ||
| all_fixed_mods.append(value["static_mods"]) | ||
| all_variable_mods.append(value["variable_mods"]) | ||
|
|
||
| # TODO need to have an actual value | ||
| params.search_engine = "" | ||
| params.search_engine_version = data["version"] | ||
| params.enzyme = data["database"]["enzyme"]["cleave_at"] | ||
| params.allowed_miscleavages = data["database"]["enzyme"]["missed_cleavages"] | ||
| params.fixed_mods = data["database"]["static_mods"] | ||
| params.variable_mods = data["database"]["variable_mods"] | ||
|
|
||
| try: | ||
| params.precursor_mass_tolerance = data["precursor_tol"]["ppm"] | ||
| except KeyError: | ||
| params.precursor_mass_tolerance = data["precursor_tol"]["Da"] | ||
|
|
||
| params.fragment_mass_tolerance = data["fragment_tol"]["ppm"] | ||
| params.min_peptide_length = data["database"]["enzyme"]["min_len"] | ||
| params.max_peptide_length = data["database"]["enzyme"]["max_len"] | ||
| params.max_mods = data["database"]["max_variable_mods"] | ||
| params.min_precursor_charge = data["precursor_charge"][0] | ||
| params.max_precursor_charge = data["precursor_charge"][1] | ||
| params.enable_match_between_runs = True | ||
|
|
||
| return params | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| """ | ||
| Extract parameters from MSAngel JSON files and save them as CSV. | ||
| """ | ||
| from pathlib import Path | ||
|
|
||
| file = Path("../../../test/params/msangel_results.json") | ||
|
|
||
| # Extract parameters from the file | ||
| params = extract_params(file) | ||
|
|
||
| # Convert the extracted parameters to a dictionary and then to a pandas Series | ||
| data_dict = params.__dict__ | ||
| series = pd.Series(data_dict) | ||
|
|
||
| # Write the Series to a CSV file | ||
| series.to_csv(file.with_suffix(".csv")) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.