[FIX] error in conformer model

README.md

@@ -10,11 +10,11 @@
 
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massdash/constants.py

@@ -23,4 +23,5 @@
 URL_TEST_OSW = "https://github.com/Roestlab/massdash/raw/dev/test/test_data/example_dia/openswath/osw/test.osw"
 URL_TEST_PQP = "https://github.com/Roestlab/massdash/raw/dev/test/test_data/example_dia/openswath/lib/test.pqp"
 URL_TEST_RAW_MZML = "https://github.com/Roestlab/massdash/raw/dev/test/test_data/example_dia/raw/test_raw_1.mzML"
-URL_TEST_DREAMDIA_REPORT = "https://github.com/Roestlab/massdash/raw/dev/test/test_data/example_dia/dreamdia/test_dreamdia_report.tsv"
+URL_TEST_DREAMDIA_REPORT = "https://github.com/Roestlab/massdash/raw/dev/test/test_data/example_dia/dreamdia/test_dreamdia_report.tsv"
+URL_PRETRAINED_CONFORMER = "https://github.com/Roestlab/massdash/releases/download/v0.0.1-alpha/base_cape.onnx"

massdash/loaders/SqMassLoader.py

@@ -81,7 +81,7 @@ def loadTransitionGroups(self, pep_id: str, charge: int) -> Dict[str, Transition
             prec_chrom_ids = t.getPrecursorChromIDs(precursor_id)
             precursor_chroms = t.getDataForChromatograms(prec_chrom_ids['chrom_ids'], prec_chrom_ids['native_ids'])
 
-            out[t] = TransitionGroup(precursor_chroms, transition_chroms)
+            out[t] = TransitionGroup(precursor_chroms, transition_chroms, pep_id, charge)
         return out
 
     def loadTransitionGroupFeaturesDf(self, pep_id: str, charge: int) -> pd.DataFrame:

massdash/loaders/access/MzMLDataAccess.py

@@ -420,7 +420,7 @@ def msExperimentToFeatureMap(self, msExperiment: po.MSExperiment, feature: Trans
         else:
             LOGGER.warn(f"No spectra found for peptide: {feature.sequence}{feature.precursor_charge}. Try adjusting the extraction parameters")
 
-        return FeatureMap(results_df, config)
+        return FeatureMap(results_df, feature.sequence, feature.precursor_charge, config)
 
     def _find_closest_reference_mz(self, given_mz: np.array, reference_mz_values: np.array, peptide_product_annotation_list: np.array) -> np.array:
         """

massdash/peakPickers/ConformerPeakPicker.py

@@ -11,8 +11,10 @@
 # Structs
 from ..structs.TransitionGroup import TransitionGroup
 from ..structs.TransitionGroupFeature import TransitionGroupFeature
+from ..loaders.SpectralLibraryLoader import SpectralLibraryLoader
 # Utils
 from ..util import check_package
+from ..util import LOGGER
 
 onnxruntime, ONNXRUNTIME_AVAILABLE = check_package("onnxruntime")
 
@@ -35,7 +37,7 @@ class ConformerPeakPicker:
         _convertConformerFeatureToTransitionGroupFeatures: Convert conformer predicted feature to TransitionGroupFeatures.
     """
 
-    def __init__(self, transition_group: TransitionGroup, pretrained_model_file: str, window_size: int = 175, prediction_threshold: float = 0.5, prediction_type: str = "logits"):
+    def __init__(self, library_file: str, pretrained_model_file: str, prediction_threshold: float = 0.5, prediction_type: str = "logits"):
         """
         Initialize the ConformerPeakPicker class.
 
@@ -46,14 +48,18 @@ def __init__(self, transition_group: TransitionGroup, pretrained_model_file: str
             prediction_threshold (float, optional): The prediction threshold for peak picking. Defaults to 0.5.
             prediction_type (str, optional): The prediction type for peak picking. Defaults to "logits".
         """
-        self.transition_group = transition_group
         self.pretrained_model_file = pretrained_model_file
-        self.window_size = window_size
         self.prediction_threshold = prediction_threshold
         self.prediction_type = prediction_type
-        self.onnx_session = None
+        self.library = SpectralLibraryLoader(library_file)
 
         self._validate_model()
+
+        ## set in load_model
+        self.onnx_session = None
+        self.window_size = None
+
+        LOGGER.name = __class__.__name__
 
     def _validate_model(self):
         """
@@ -73,8 +79,14 @@ def load_model(self):
             raise ImportError("onnxruntime is required for loading the pretrained Conformer model, but not installed.")
         # Load pretrained model
         self.onnx_session = onnxruntime.InferenceSession(self.pretrained_model_file)
+        if len(self.onnx_session.get_inputs()) == 0:
+            raise ValueError("Pretrained model does not have any inputs.")
+        elif len(self.onnx_session.get_inputs()[0].shape) != 3:
+            raise ValueError("First input to model must be a 3D numpy array, current shape: {}".format(len(self.onnx_session.get_inputs()[0].shape)))
+        else:
+            self.window_size = self.onnx_session.get_inputs()[0].shape[2]
 
-    def pick(self, max_int_transition: int=1000) -> List[TransitionGroupFeature]:
+    def pick(self, transition_group, max_int_transition: int=1000) -> List[TransitionGroupFeature]:
         """
         Perform peak picking.
 
@@ -85,19 +97,19 @@ def pick(self, max_int_transition: int=1000) -> List[TransitionGroupFeature]:
             List[TransitionGroupFeature]: The list of transition group features.
         """
         # Transform data into required input
-        print("Preprocessing data...")
-        conformer_preprocessor = ConformerPreprocessor(self.transition_group)
-        input_data = conformer_preprocessor.preprocess()
-        print("Loading model...")
+        LOGGER.info("Loading model...")
         self.load_model()
-        print("Predicting...")
+        LOGGER.info("Preprocessing data...")
+        conformer_preprocessor = ConformerPreprocessor(transition_group, self.window_size)
+        input_data = conformer_preprocessor.preprocess(self.library)
+        LOGGER.info("Predicting...")
         ort_input = {self.onnx_session.get_inputs()[0].name: input_data}
         ort_output = self.onnx_session.run(None, ort_input)
-        print("Getting predicted boundaries...")
+        LOGGER.info("Getting predicted boundaries...")
         peak_info = conformer_preprocessor.find_top_peaks(ort_output[0], ["precursor"], self.prediction_threshold, self.prediction_type)
         # Get actual peak boundaries
-        peak_info = conformer_preprocessor.get_peak_boundaries(peak_info, self.transition_group, self.window_size)
-        print(f"Peak info: {peak_info}")
+        peak_info = conformer_preprocessor.get_peak_boundaries(peak_info)
+        LOGGER.info(f"Peak info: {peak_info}")
         return self._convertConformerFeatureToTransitionGroupFeatures(peak_info, max_int_transition)
 
     def _convertConformerFeatureToTransitionGroupFeatures(self, peak_info: dict, max_int_transition: int=1000) -> List[TransitionGroupFeature]:

massdash/preprocess/ConformerPreprocessor.py

@@ -10,6 +10,7 @@
 from .GenericPreprocessor import GenericPreprocessor 
 # Structs
 from ..structs.TransitionGroup import TransitionGroup
+from ..loaders.SpectralLibraryLoader import SpectralLibraryLoader
 # Utils
 from ..util import check_package
 
@@ -36,9 +37,13 @@ class ConformerPreprocessor(GenericPreprocessor):
 
     """
 
-    def __init__(self, transition_group: TransitionGroup):
+    def __init__(self, transition_group: TransitionGroup, window_size: int=175):
         super().__init__(transition_group)
 
+        ## pad the transition group to the window size
+        self.transition_group = self.transition_group.adjust_length(window_size)
+        self.window_size = window_size
+
     @staticmethod
     def min_max_scale(data, min: float=None, max: float=None) -> np.ndarray:
         """
@@ -101,14 +106,14 @@ def sigmoid(x: np.ndarray) -> np.ndarray:
         """
         return 1 / (1 + np.exp(-x))
 
-    def preprocess(self, window_size: int=175) -> np.ndarray:
+    def preprocess(self, library: SpectralLibraryLoader) -> np.ndarray:
         """
         Preprocesses the data by scaling and transforming it into a numpy array.
 
         Code adapted from CAPE
 
         Args:
-            window_size (int): The desired window size for trimming the data. Default is 175.
+            SpectralLibraryLoader (SpectralLibraryLoader): The spectral library loader.
 
         Returns:
             np.ndarray: The preprocessed data as a numpy array with shape (1, 21, len(data[0])).
@@ -122,17 +127,19 @@ def preprocess(self, window_size: int=175) -> np.ndarray:
             # Row index 19: library retention time diff
             # Row index 20: precursor charge
 
-        #  initialize empty numpy array
-        data = np.empty((0, len(self.transition_group.transitionData[0].intensity)), float)
+        if len(self.transition_group.transitionData) != 6:
+            raise ValueError(f"Transition group must have 6 transitions, but has {len(self.transition_group.transitionData)}.")
 
-        lib_int_data = np.empty((0, len(self.transition_group.transitionData[0].intensity)), float)
+        #  initialize empty numpy array
+        data = np.empty((0, self.window_size), float) 
+        lib_int_data = np.empty((0, self.window_size), float)
 
         for chrom in self.transition_group.transitionData:
             # append ms2 intensity data to data
             data = np.append(data, [chrom.intensity], axis=0)
 
-            lib_int =  self.transition_group.targeted_transition_list[self.transition_group.targeted_transition_list.Annotation==chrom.label]['LibraryIntensity'].values 
-            lib_int = np.repeat(lib_int, len(chrom.intensity))
+            lib_int = library.get_fragment_library_intensity(self.transition_group.sequence, self.transition_group.precursor_charge, chrom.label)
+            lib_int = np.repeat(lib_int, self.window_size)
             lib_int_data = np.append(lib_int_data, [lib_int], axis=0)
 
         # initialize empty numpy array to store scaled data
@@ -148,20 +155,7 @@ def preprocess(self, window_size: int=175) -> np.ndarray:
             )
 
         ## MS1 trace data
-        # padd precursor intensity data with zeros to match ms2 intensity data
-        len_trans = len(self.transition_group.transitionData[0].intensity)
-        len_prec = len(self.transition_group.precursorData[0].intensity)
-        if len_prec!=len_trans:
-            if len_prec < len_trans:
-                prec_int = np.pad(self.transition_group.precursorData[0].intensity, (0, len_trans-len_prec), 'constant', constant_values=(0, 0))
-            if len_prec > len_trans:
-                prec_int = self.transition_group.precursorData[0].intensity
-                # compute number of points to trim from either side of the middle point
-                remove_n_points = len_prec - len_trans
-                # trim precursor intensity data
-                prec_int = prec_int[remove_n_points//2:-remove_n_points//2]
-        else:
-            prec_int = self.transition_group.precursorData[0].intensity
+        prec_int = self.transition_group.precursorData[0].intensity
 
         # append ms1 intensity data to data
         new_data[12] = self.min_max_scale(prec_int)
@@ -190,18 +184,11 @@ def preprocess(self, window_size: int=175) -> np.ndarray:
         new_data[19] = tmp_arr
 
         ## Add charge state
-        new_data[20] = self.transition_group.targeted_transition_list.PrecursorCharge.values[0] * np.ones(len(data[0]))
+        new_data[20] = self.transition_group.precursor_charge * np.ones(len(data[0]))
 
         ## Convert to float32
         new_data = new_data.astype(np.float32)
 
-        ## trim data if does not match window size starting at the centre
-        if len(new_data[0]) > window_size:
-            middle_index = len(data[0]) // 2
-            trim_start = middle_index - (window_size // 2)
-            trim_end = middle_index + (window_size // 2) + 1
-            new_data = new_data[:, trim_start:trim_end]
-
         # cnvert the shape to be (1, 21, len(data[0]))
         new_data = np.expand_dims(new_data, axis=0)
 
@@ -297,43 +284,42 @@ def find_top_peaks(self, preds, seq_classes: List[str]='input_precursor', thresh
 
         return peak_info
 
-    def get_peak_boundaries(self, peak_info: dict, tr_group: TransitionGroup, window_size: int=175):
+    def get_peak_boundaries(self, peak_info: dict):
         """
         Adjusts the peak boundaries in the peak_info dictionary based on the window size and the dimensions of the input rt_array.
         Calculates the actual RT values from the rt_array and appends them to the peak_info dictionary.
 
         Args:
             peak_info (dict): A dictionary containing information about the peaks.
-            tr_group (TransitionGroup): The transition group containing the data.
             window_size (int, optional): The size of the window used for trimming the rt_array. Defaults to 175.
 
         Returns:
             dict: The updated peak_info dictionary with adjusted peak boundaries and RT values.
         """
-        rt_array = tr_group.transitionData[0].data 
-        if rt_array.shape[0] != window_size:
-            print(f"input_data {rt_array.shape[0]} was trimmed to {window_size}, adjusting peak_info indexes to map to the original datas dimensions")
+        rt_array = self.transition_group.transitionData[0].data 
+        if rt_array.shape[0] != self.window_size:
+            print(f"input_data {rt_array.shape[0]} was trimmed to {self.window_size}, adjusting peak_info indexes to map to the original datas dimensions")
             for key in peak_info.keys():
                 for i in range(len(peak_info[key])):
                     peak_info[key][i]['max_idx_org'] = peak_info[key][i]['max_idx']
                     peak_info[key][i]['start_idx_org'] = peak_info[key][i]['start_idx']
                     peak_info[key][i]['end_idx_org'] = peak_info[key][i]['end_idx']
-                    new_max_idx = peak_info[key][i]['max_idx'] + (window_size // 2) - (rt_array.shape[0] // 2)
+                    new_max_idx = peak_info[key][i]['max_idx'] + (self.window_size // 2) - (rt_array.shape[0] // 2)
                     if not new_max_idx < 0:
                         peak_info[key][i]['max_idx'] = new_max_idx
 
-                    new_start_idx = peak_info[key][i]['start_idx'] + (window_size // 2) - (rt_array.shape[0] // 2)
+                    new_start_idx = peak_info[key][i]['start_idx'] + (self.window_size // 2) - (rt_array.shape[0] // 2)
                     if not new_start_idx < 0:
                         peak_info[key][i]['start_idx'] = new_start_idx
 
-                    peak_info[key][i]['end_idx'] = peak_info[key][i]['end_idx'] + (window_size // 2) - (rt_array.shape[0] // 2)
+                    peak_info[key][i]['end_idx'] = peak_info[key][i]['end_idx'] + (self.window_size // 2) - (rt_array.shape[0] // 2)
 
         # get actual RT value from RT array and append to peak_info
         for key in peak_info.keys():
             for i in range(len(peak_info[key])):
                 peak_info[key][i]['rt_apex'] = rt_array[peak_info[key][i]['max_idx']]
                 peak_info[key][i]['rt_start'] = rt_array[peak_info[key][i]['start_idx']]
                 peak_info[key][i]['rt_end'] = rt_array[peak_info[key][i]['end_idx']]
-                peak_info[key][i]['int_apex'] = np.max([tg.intensity[peak_info[key][i]['max_idx']] for tg in tr_group.transitionData])
+                peak_info[key][i]['int_apex'] = np.max([tg.intensity[peak_info[key][i]['max_idx']] for tg in self.transition_group.transitionData])
 
         return peak_info

massdash/server/ExtractedIonChromatogramAnalysisServer.py

@@ -163,10 +163,10 @@ def main(self):
                         tr_group.targeted_transition_list = transition_list_ui.target_transition_list
                         print(f"Pretrained model file: {peak_picking_settings.peak_picker_algo_settings.pretrained_model_file}")
 
-                        peak_picker = ConformerPeakPicker(tr_group, peak_picking_settings.peak_picker_algo_settings.pretrained_model_file, window_size=peak_picking_settings.peak_picker_algo_settings.conformer_window_size, prediction_threshold=peak_picking_settings.peak_picker_algo_settings.conformer_prediction_threshold, prediction_type=peak_picking_settings.peak_picker_algo_settings.conformer_prediction_type)
+                        peak_picker = ConformerPeakPicker(self.massdash_gui.file_input_settings.osw_file_path, peak_picking_settings.peak_picker_algo_settings.pretrained_model_file, window_size=peak_picking_settings.peak_picker_algo_settings.conformer_window_size, prediction_threshold=peak_picking_settings.peak_picker_algo_settings.conformer_prediction_threshold, prediction_type=peak_picking_settings.peak_picker_algo_settings.conformer_prediction_type)
                         # get the trantition in tr_group with the max intensity
                         max_int_transition = np.max([transition.intensity for transition in tr_group.transitionData])
-                        peak_features = peak_picker.pick(max_int_transition)
+                        peak_features = peak_picker.pick(tr_group, max_int_transition)
                         tr_group_feature_data[file.filename] = peak_features
                 st.write(f"Performing Conformer Peak Picking... Elapsed time: {elapsed_time()}")
             else:

massdash/server/OneDimensionPlotterServer.py

@@ -46,12 +46,13 @@ class OneDimensionPlotterServer:
     def __init__(self, 
                  feature_map_dict: Dict[str, FeatureMap], 
                  transition_list_ui: TransitionListUISettings, chrom_plot_settings: ChromatogramPlotUISettings, 
-                 peak_picking_settings: PeakPickingUISettings,
+                 peak_picking_settings: PeakPickingUISettings, spectral_library_path: str=None,
                  verbose: bool=False):
         self.feature_map_dict = feature_map_dict 
         self.transition_list_ui = transition_list_ui
         self.chrom_plot_settings = chrom_plot_settings
         self.peak_picking_settings = peak_picking_settings
+        self.spectral_library_path = spectral_library_path
         self.plot_obj_dict = {}
         self.verbose = verbose
 
@@ -74,7 +75,7 @@ def generate_chromatogram_plots(self):
                 tr_group = feature_map.to_chromatograms()
                 # Perform peak picking if enabled
                 peak_picker = PeakPickingServer(self.peak_picking_settings, self.chrom_plot_settings)
-                tr_group_feature_data = peak_picker.perform_peak_picking(tr_group_data={'tmp':tr_group}, transition_list_ui=self.transition_list_ui)
+                tr_group_feature_data = peak_picker.perform_peak_picking(tr_group_data={'tmp':tr_group}, transition_list_ui=self.transition_list_ui, spec_lib=self.spectral_library_path)
                 plot_settings_dict = self._get_plot_settings('Retention Time (s)', 'Intensity', file, 'chromatogram')
                 plot_obj = self._generate_plot(tr_group, plot_settings_dict, tr_group_feature_data['tmp'])
                 run_plots_list.append(plot_obj)