Add documentation and docstrings, improve DIA-NN parsing, refactoring

docs/index.rst

@@ -77,6 +77,11 @@ People who contributed to ProteoBench (in alphabetical order)
     *VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium*
     *Department of Biomolecular Medicine, UGent, Ghent, Belgium*
 
+.. line-block::
+    **Robbe Devreese**
+    *VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium*
+    *Department of Biomolecular Medicine, UGent, Ghent, Belgium*
+
 .. line-block:: 
     **Nadezhda T. Doncheva**
     *Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark*
@@ -90,6 +95,11 @@ People who contributed to ProteoBench (in alphabetical order)
     *VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium*
     *Department of Biomolecular Medicine, UGent, Ghent, Belgium*
 
+.. line-block::
+    **Caroline Jachmann**
+    *VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium*
+    *Department of Biomolecular Medicine, UGent, Ghent, Belgium*
+
 .. line-block:: 
     **Vedran Kasalica**
     *Netherlands eScience Center, Science Park 402, 1098 XH, Amsterdam, The Netherlands*
@@ -112,6 +122,11 @@ People who contributed to ProteoBench (in alphabetical order)
     *Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III - Paul Sabatier (UT3), Toulouse, France*
     *Infrastructure nationale de protéomique, ProFI, FR 2048, Toulouse, France*
 
+.. line-block::
+    **Alireza Nameni**
+    *VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium*
+    *Department of Biomolecular Medicine, UGent, Ghent, Belgium*
+
 .. line-block::
     **Martin Rykær**
     *Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark*
@@ -125,6 +140,11 @@ People who contributed to ProteoBench (in alphabetical order)
     *Department of Biomolecular Medicine, Ghent University, Ghent, Belgium*
     *VIB - UGent Center for Medical Biotechnology, VIB, Ghent, Belgium*
 
+.. line-block::
+    **Sam van Puyenbroeck**
+    *VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium*
+    *Department of Biomolecular Medicine, UGent, Ghent, Belgium*
+
 .. line-block::
     **Bart Van Puyvelde**
     *ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, Belgium*

proteobench/io/params/__init__.py

@@ -55,6 +55,17 @@ class ProteoBenchParameters:
         Minimum precursor charge allowed.
     max_precursor_charge : Optional[int]
         Maximum precursor charge allowed.
+    spectral_library_generation : Optional[dict]
+        Models used to generate spectral library (DIA-specific).
+    scan_window : Optional[int]
+        Scan window radius. Ideally corresponds to approximate
+        average number of data points per peak (DIA-specific).
+    quantification_method_DIANN : Optional[str]
+        Quantification strategy used in the DIA-NN engine (DIANN-specific).
+    second_pass : Optional[bool]
+        Whether second pass search is enabled (DIANN-specific).
+    protein_inference : Optional[str]
+        Protein inference method used.
     """
 
     software_name: Optional[str] = None
@@ -77,3 +88,8 @@ class ProteoBenchParameters:
     max_mods: Optional[int] = None  # max_num_modifications
     min_precursor_charge: Optional[int] = None  # precursor_charge
     max_precursor_charge: Optional[int] = None
+    scan_window: Optional[int] = None  # DIA-specific
+    quantification_method_DIANN: Optional[str] = None  # DIANN-specific
+    second_pass: Optional[bool] = None  # DIANN specific
+    protein_inference: Optional[str] = None
+    predictors_library: Optional[dict] = None

proteobench/io/params/alphadia.py

@@ -0,0 +1,183 @@
+import re
+from proteobench.io.params import ProteoBenchParameters
+import pathlib
+import pandas as pd
+
+levels = [0, 1, 5, 9, 13, 17]
+
+ANSI_REGEX = re.compile(r"(\x9B|\x1B\[)[0-?]*[ -\/]*[@-~]")
+
+
+def parse_line(line):
+    # Remove the info part and convert ansi
+    line = ANSI_REGEX.sub("", line[22:].strip())
+    # Split the string to tab part and setting part
+    tab, setting = line.split("──")
+    setting_list = setting.split(":")
+    if len(setting_list) == 1:
+        setting_dict = {setting_list[0]: None}
+    else:
+        setting_dict = {setting_list[0]: setting_list[1]}
+    # Convert tab to level
+    level = levels.index(len(tab))
+
+    # Return header, parsed setting, and the level
+    return setting_list[0], setting_dict, level
+
+
+def parse_section(
+    line,
+    line_generator,
+):
+    section = {}
+
+    # Parse the line (both level and dictionary)
+    header_prev, line_dict, level_prev = line
+    section.update(line_dict)
+
+    try:
+        # Get the next line to know what to do
+        next_line = next(line_generator)
+        header_next, line_dict_next, level_next = parse_line(next_line)
+    except:
+        # If no lines left, go up a level, returning the sectino so far
+        return section, 0, None
+
+    while True:
+        # If no more lines go up a level
+        try:
+            header_next, line_dict_next, level_next = parse_line(next_line)
+        except:
+            break
+
+        # If the next line is start of new section again
+        if level_next > level_prev:
+            # Get the subsection
+
+            subsection, _, next_line = parse_section(
+                line=parse_line(next_line),
+                line_generator=line_generator,
+            )
+            # Add this subsection to new section
+            # A new line is already outputted so continue
+            section[header_prev] = subsection
+            continue
+
+        # if new line is at same level
+        elif level_prev == level_next:
+            section.update(line_dict_next)
+            header_prev = header_next
+            level_prev = level_next
+            try:
+                next_line = next(line_generator)
+            except:
+                break
+
+        # The next line needs to go up and output the section
+        # Also the new line should be returned
+        else:
+            return section, level_next, next_line
+
+    return section, 0, None
+
+
+def extract_file_version(line):
+    # Regex pattern to extract the version number
+    version_pattern = r"version:\s*([\d\.]+)"
+
+    # Search for the version number in the line
+    match = re.search(version_pattern, line)
+
+    # Extract and print the version number if found
+    version = match.group(1) if match else None
+    return version
+
+
+def add_fdr_parameters(parameter_dict, parsed_settings):
+    fdr_value = float(parsed_settings["fdr"]["fdr"])
+    fdr_level = parsed_settings["fdr"]["group_level"].strip()
+
+    level_mapping = {"proteins": "ident_fdr_protein"}
+    fdr_parameters = {"ident_fdr_psm": None, "ident_fdr_peptide": None, "ident_fdr_protein": None}
+    fdr_parameters[level_mapping[fdr_level]] = fdr_value
+    parameter_dict.update(fdr_parameters)
+
+
+def get_min_max(list_of_elements):
+    if "(user defined)" in list_of_elements[1]:
+        min_value = int(list_of_elements[1].replace("(user defined)", ""))
+        if len(list_of_elements) == 4:
+            max_value = int(list_of_elements[3].replace("(user defined)", ""))
+        else:
+            max_value = int(list_of_elements[2])
+    else:
+        min_value = int(list_of_elements[0])
+        if len(list_of_elements) == 3:
+            max_value = int(list_of_elements[2].replace("(user defined)", ""))
+        else:
+            max_value = int(list_of_elements[1])
+    return min_value, max_value
+
+
+def extract_params(fname):
+    with open(fname) as f:
+        lines_read = f.readlines()
+        lines = [line for line in lines_read if "──" in line]
+
+    version = extract_file_version(lines_read[6])
+
+    line_generator = iter(lines)
+    first_line = next(line_generator)
+
+    parsed_settings, level, line = parse_section(line=parse_line(first_line), line_generator=line_generator)
+
+    peptide_lengths = get_min_max(list(parsed_settings["library_prediction"]["precursor_len"].keys()))
+    precursor_charges = get_min_max(list(parsed_settings["library_prediction"]["precursor_charge"].keys()))
+
+    if "(user defined)" in parsed_settings["search"]["target_ms1_tolerance"]:
+        prec_tol = float(parsed_settings["search"]["target_ms1_tolerance"].replace("(user defined)", ""))
+    else:
+        prec_tol = float(parsed_settings["search"]["target_ms1_tolerance"])
+    if "(user defined)" in parsed_settings["search"]["target_ms2_tolerance"]:
+        frag_tol = float(parsed_settings["search"]["target_ms2_tolerance"].replace("(user defined)", ""))
+    else:
+        frag_tol = float(parsed_settings["search"]["target_ms2_tolerance"])
+
+    parameters = {
+        "software_name": "AlphaDIA",
+        "search_engine": "AlphaDIA",
+        "software_version": version,
+        "search_engine_version": version,
+        "enable_match_between_runs": "?",
+        "precursor_mass_tolerance": prec_tol,
+        "fragment_mass_tolerance": frag_tol,
+        "enzyme": parsed_settings["library_prediction"]["enzyme"].strip(),
+        "allowed_miscleavages": int(parsed_settings["library_prediction"]["missed_cleavages"]),
+        "min_peptide_length": peptide_lengths[0],
+        "max_peptide_length": peptide_lengths[1],
+        "min_precursor_charge": precursor_charges[0],
+        "max_precursor_charge": precursor_charges[1],
+        "fixed_mods": parsed_settings["library_prediction"]["fixed_modifications"].strip(),
+        "variable_mods": parsed_settings["library_prediction"]["variable_modifications"].strip(),
+        "max_mods": int(parsed_settings["library_prediction"]["max_var_mod_num"].replace("(user defined)", "")),
+        "scan_window": int(parsed_settings["selection_config"]["max_size_rt"].replace("(user defined)", "")),
+        "quantification_method_DIANN": None,
+        "second_pass": None,
+        "protein_inference": parsed_settings["fdr"]["inference_strategy"].strip(),
+        "predictors_library": "Built-in",
+    }
+
+    add_fdr_parameters(parameters, parsed_settings)
+    return ProteoBenchParameters(**parameters)
+
+
+if __name__ == "__main__":
+    for fname in [
+        "../../../test/params/log_alphadia_1.txt",
+        "../../../test/params/log_alphadia_2.txt",
+    ]:
+        file = pathlib.Path(fname)
+        params = extract_params(file)
+        data_dict = params.__dict__
+        series = pd.Series(data_dict)
+        series.to_csv(file.with_suffix(".csv"))