Revert "Merge branch 'timsRescore' into main"

This reverts commit ff81bd9, reversing
changes made to 23dfa95.

.github/workflows/publish.yml

@@ -14,7 +14,7 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v4
         with:
           python-version: "3.11"
 
@@ -47,7 +47,7 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@v4
         with:
           python-version: "3.11"
 

.github/workflows/test.yml

@@ -24,10 +24,14 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install ruff
+          pip install flake8
 
-      - name: Run Ruff
-        run: ruff check --output-format=github .
+      - name: Lint with flake8
+        run: |
+          # stop the build if there are Python syntax errors or undefined names
+          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
 
       - name: Build and install ms2rescore package
         run: |

Dockerfile

@@ -1,10 +1,8 @@
-FROM python:3.10
-
-# ARG DEBIAN_FRONTEND=noninteractive
+FROM ubuntu:focal
 
 LABEL name="ms2rescore"
 
-# ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/ms2rescore
+ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/ms2rescore
 
 ADD pyproject.toml /ms2rescore/pyproject.toml
 ADD LICENSE /ms2rescore/LICENSE
@@ -13,7 +11,8 @@ ADD MANIFEST.in /ms2rescore/MANIFEST.in
 ADD ms2rescore /ms2rescore/ms2rescore
 
 RUN apt-get update \
-    && apt install -y procps git-lfs \
-    && pip install /ms2rescore
+    && apt-get install --no-install-recommends -y python3-pip procps libglib2.0-0 libsm6 libxrender1 libxext6 \
+    && rm -rf /var/lib/apt/lists/* \
+    && pip3 install ms2rescore/
 
 ENTRYPOINT [""]

docs/source/config_schema.md

@@ -10,7 +10,6 @@
     - **`deeplc`**: Refer to *[#/definitions/deeplc](#definitions/deeplc)*.
     - **`maxquant`**: Refer to *[#/definitions/maxquant](#definitions/maxquant)*.
     - **`ionmob`**: Refer to *[#/definitions/ionmob](#definitions/ionmob)*.
-    - **`im2deep`**: Refer to *[#/definitions/im2deep](#definitions/im2deep)*.
   - **`rescoring_engine`** *(object)*: Rescoring engine to use and its configuration. Leave empty to skip rescoring and write features to file. Default: `{"mokapot": {}}`.
     - **`.*`**: Refer to *[#/definitions/rescoring_engine](#definitions/rescoring_engine)*.
     - **`percolator`**: Refer to *[#/definitions/percolator](#definitions/percolator)*.
@@ -48,14 +47,6 @@
     - **One of**
       - *string*
       - *null*
-  - **`psm_id_rt_pattern`**: Regex pattern to extract retention time from PSM identifier. Requires at least one capturing group. Default: `null`.
-    - **One of**
-      - *string*
-      - *null*
-  - **`psm_id_im_pattern`**: Regex pattern to extract ion mobility from PSM identifier. Requires at least one capturing group. Default: `null`.
-    - **One of**
-      - *string*
-      - *null*
   - **`lower_score_is_better`** *(boolean)*: Bool indicating if lower score is better. Default: `false`.
   - **`modification_mapping`** *(object)*: Mapping of modification labels to each replacement label. Default: `{}`.
   - **`fixed_modifications`** *(object)*: Mapping of amino acids with fixed modifications to the modification name. Can contain additional properties. Default: `{}`.
@@ -84,8 +75,6 @@
   - **`ionmob_model`** *(string)*: Path to Ionmob model directory. Default: `"GRUPredictor"`.
   - **`reference_dataset`** *(string)*: Path to Ionmob reference dataset file. Default: `"Meier_unimod.parquet"`.
   - **`tokenizer`** *(string)*: Path to tokenizer json file. Default: `"tokenizer.json"`.
-- <a id="definitions/im2deep"></a>**`im2deep`** *(object)*: Ion mobility feature generator configuration using IM2Deep. Can contain additional properties. Refer to *[#/definitions/feature_generator](#definitions/feature_generator)*.
-  - **`reference_dataset`** *(string)*: Path to IM2Deep reference dataset file. Default: `"Meier_unimod.parquet"`.
 - <a id="definitions/mokapot"></a>**`mokapot`** *(object)*: Mokapot rescoring engine configuration. Additional properties are passed to the Mokapot brew function. Can contain additional properties. Refer to *[#/definitions/rescoring_engine](#definitions/rescoring_engine)*.
   - **`write_weights`** *(boolean)*: Write Mokapot weights to a text file. Default: `false`.
   - **`write_txt`** *(boolean)*: Write Mokapot results to a text file. Default: `false`.

ms2rescore/core.py

@@ -8,7 +8,7 @@
 
 from ms2rescore.feature_generators import FEATURE_GENERATORS
 from ms2rescore.parse_psms import parse_psms
-from ms2rescore.parse_spectra import fill_missing_values
+from ms2rescore.parse_spectra import get_missing_values
 from ms2rescore.report import generate
 from ms2rescore.rescoring_engines import mokapot, percolator
 from ms2rescore import exceptions
@@ -59,17 +59,11 @@ def rescore(configuration: Dict, psm_list: Optional[PSMList] = None) -> None:
     )
 
     # TODO: avoid hard coding feature generators in some way
-    rt_required = ("deeplc" in config["feature_generators"]) and (
-        None in psm_list["retention_time"]
-    )
-    im_required = ("ionmob" or "im2deep" in config["feature_generators"]) and (
-        None in psm_list["ion_mobility"]
-    )
-    logger.debug(f"RT required: {rt_required}, IM required: {im_required}")
-
+    rt_required = "deeplc" in config["feature_generators"] and None in psm_list["retention_time"]
+    im_required = "ionmob" in config["feature_generators"] and None in psm_list["ion_mobility"]
     if rt_required or im_required:
         logger.info("Parsing missing retention time and/or ion mobility values from spectra...")
-        fill_missing_values(config, psm_list, missing_rt=rt_required, missing_im=im_required)
+        get_missing_values(config, psm_list, missing_rt=rt_required, missing_im=im_required)
 
     # Add rescoring features
     for fgen_name, fgen_config in config["feature_generators"].items():

ms2rescore/feature_generators/__init__.py

@@ -7,13 +7,11 @@
 from ms2rescore.feature_generators.ionmob import IonMobFeatureGenerator
 from ms2rescore.feature_generators.maxquant import MaxQuantFeatureGenerator
 from ms2rescore.feature_generators.ms2pip import MS2PIPFeatureGenerator
-from ms2rescore.feature_generators.im2deep import IM2DeepFeatureGenerator
 
 FEATURE_GENERATORS = {
     "basic": BasicFeatureGenerator,
     "ms2pip": MS2PIPFeatureGenerator,
     "deeplc": DeepLCFeatureGenerator,
     "maxquant": MaxQuantFeatureGenerator,
     "ionmob": IonMobFeatureGenerator,
-    "im2deep": IM2DeepFeatureGenerator,
 }

ms2rescore/feature_generators/deeplc.py

@@ -21,10 +21,12 @@
 from collections import defaultdict
 from inspect import getfullargspec
 from itertools import chain
-from typing import List, Union
+from typing import List, Optional, Union
 
 import numpy as np
+import pandas as pd
 from psm_utils import PSMList
+from psm_utils.io import peptide_record
 
 from ms2rescore.feature_generators.base import FeatureGeneratorBase
 
@@ -39,7 +41,8 @@ def __init__(
         self,
         *args,
         lower_score_is_better: bool = False,
-        calibration_set_size: Union[int, float, None] = None,
+        calibration_set_size: Union[int, float] = 0.15,
+        spectrum_path: Optional[str] = None,
         processes: int = 1,
         **kwargs,
     ) -> None:
@@ -56,6 +59,9 @@ def __init__(
         calibration_set_size: int or float
             Amount of best PSMs to use for DeepLC calibration. If this value is lower
             than the number of available PSMs, all PSMs will be used. (default: 0.15)
+        spectrum_path
+            Path to spectrum file or directory with spectrum files. If None, inferred from `run`
+            field in PSMs. Defaults to None.
         processes: {int, None}
             Number of processes to use in DeepLC. Defaults to 1.
         kwargs: dict
@@ -71,6 +77,7 @@ def __init__(
 
         self.lower_psm_score_better = lower_score_is_better
         self.calibration_set_size = calibration_set_size
+        self.spectrum_path = spectrum_path
         self.processes = processes
         self.deeplc_kwargs = kwargs or {}
 
@@ -144,15 +151,17 @@ def add_features(self, psm_list: PSMList) -> None:
                     # Make new PSM list for this run (chain PSMs per spectrum to flat list)
                     psm_list_run = PSMList(psm_list=list(chain.from_iterable(psms.values())))
 
+                    logger.debug("Calibrating DeepLC...")
                     psm_list_calibration = self._get_calibration_psms(psm_list_run)
-                    logger.debug(f"Calibrating DeepLC with {len(psm_list_calibration)} PSMs...")
                     self.deeplc_predictor = self.DeepLC(
                         n_jobs=self.processes,
                         verbose=self._verbose,
                         path_model=self.selected_model or self.user_model,
                         **self.deeplc_kwargs,
                     )
-                    self.deeplc_predictor.calibrate_preds(psm_list_calibration)
+                    self.deeplc_predictor.calibrate_preds(
+                        seq_df=self._psm_list_to_deeplc_peprec(psm_list_calibration)
+                    )
                     # Still calibrate for each run, but do not try out all model options.
                     # Just use model that was selected based on first run
                     if not self.selected_model:
@@ -165,7 +174,11 @@ def add_features(self, psm_list: PSMList) -> None:
                         )
 
                     logger.debug("Predicting retention times...")
-                    predictions = np.array(self.deeplc_predictor.make_preds(psm_list_run))
+                    predictions = np.array(
+                        self.deeplc_predictor.make_preds(
+                            seq_df=self._psm_list_to_deeplc_peprec(psm_list_run)
+                        )
+                    )
                     observations = psm_list_run["retention_time"]
                     rt_diffs_run = np.abs(predictions - observations)
 
@@ -191,25 +204,25 @@ def add_features(self, psm_list: PSMList) -> None:
                         )
                 current_run += 1
 
+    # TODO: Remove when DeepLC supports PSMList directly
+    @staticmethod
+    def _psm_list_to_deeplc_peprec(psm_list: PSMList) -> pd.DataFrame:
+        peprec = peptide_record.to_dataframe(psm_list)
+        peprec = peprec.rename(
+            columns={
+                "observed_retention_time": "tr",
+                "peptide": "seq",
+            }
+        )[["tr", "seq", "modifications"]]
+        return peprec
+
     def _get_calibration_psms(self, psm_list: PSMList):
         """Get N best scoring target PSMs for calibration."""
         psm_list_targets = psm_list[~psm_list["is_decoy"]]
-        if self.calibration_set_size:
-            n_psms = self._get_number_of_calibration_psms(psm_list_targets)
-            indices = np.argsort(psm_list_targets["score"])
-            indices = indices[:n_psms] if self.lower_psm_score_better else indices[-n_psms:]
-            return psm_list_targets[indices]
-        else:
-            identified_psms = psm_list_targets[psm_list_targets["qvalue"] <= 0.01]
-            if len(identified_psms) == 0:
-                raise ValueError(
-                    "No target PSMs with q-value <= 0.01 found. Please set calibration set size for calibrating deeplc."
-                )
-            elif (len(identified_psms) < 500) & (self.deeplc_kwargs["deeplc_retrain"]):
-                logger.warning(
-                    " Less than 500 target PSMs with q-value <= 0.01 found for retraining. Consider turning of deeplc_retrain, as this is likely not enough data for retraining."
-                )
-            return identified_psms
+        n_psms = self._get_number_of_calibration_psms(psm_list_targets)
+        indices = np.argsort(psm_list_targets["score"])
+        indices = indices[:n_psms] if self.lower_psm_score_better else indices[-n_psms:]
+        return psm_list_targets[indices]
 
     def _get_number_of_calibration_psms(self, psm_list):
         """Get number of calibration PSMs given `calibration_set_size` and total number of PSMs."""