Skip spectra with few peaks

casanovo/config.py

@@ -34,6 +34,7 @@ class Config:
     _default_config = Path(__file__).parent / "config.yaml"
     _config_types = dict(
         random_seed=int,
+        min_n_peaks=int,
         n_peaks=int,
         min_mz=float,
         max_mz=float,

casanovo/config.yaml

@@ -52,6 +52,8 @@ model_save_folder_path: ""
 val_check_interval: 50_000
 
 # SPECTRUM PROCESSING OPTIONS
+# Min number of peaks allowed in a spectrum
+min_n_peaks: 20
 # Number of the most intense peaks to retain, any remaining peaks are discarded
 n_peaks: 150
 # Min peak m/z allowed, peaks with smaller m/z are discarded

casanovo/data/datasets.py

@@ -16,6 +16,9 @@ class SpectrumDataset(Dataset):
     ----------
     spectrum_index : depthcharge.data.SpectrumIndex
         The MS/MS spectra to use as a dataset.
+    min_n_peaks : int
+        Minimum number of peaks allowed in each spectrum. Spectra with fewer
+        peaks are discarded.
     n_peaks : Optional[int]
         The number of top-n most intense peaks to keep in each spectrum. `None`
         retains all peaks.
@@ -38,6 +41,7 @@ class SpectrumDataset(Dataset):
     def __init__(
         self,
         spectrum_index: depthcharge.data.SpectrumIndex,
+        min_n_peaks: int = 20,
         n_peaks: int = 150,
         min_mz: float = 140.0,
         max_mz: float = 2500.0,
@@ -47,6 +51,7 @@ def __init__(
     ):
         """Initialize a SpectrumDataset"""
         super().__init__()
+        self.min_n_peaks = min_n_peaks
         self.n_peaks = n_peaks
         self.min_mz = min_mz
         self.max_mz = max_mz
@@ -86,12 +91,13 @@ def __getitem__(
         spectrum = self._process_peaks(
             mz_array, int_array, precursor_mz, precursor_charge
         )
-        return (
-            spectrum,
-            precursor_mz,
-            precursor_charge,
-            self.get_spectrum_id(idx),
-        )
+        if spectrum is not None:
+            return (
+                spectrum,
+                precursor_mz,
+                precursor_charge,
+                self.get_spectrum_id(idx),
+            )
 
     def get_spectrum_id(self, idx: int) -> Tuple[str, str]:
         """
@@ -148,13 +154,13 @@ def _process_peaks(
         )
         try:
             spectrum.set_mz_range(self.min_mz, self.max_mz)
-            if len(spectrum.mz) == 0:
+            if len(spectrum.mz) < self.min_n_peaks:
                 raise ValueError
             spectrum.remove_precursor_peak(self.remove_precursor_tol, "Da")
-            if len(spectrum.mz) == 0:
+            if len(spectrum.mz) < self.min_n_peaks:
                 raise ValueError
             spectrum.filter_intensity(self.min_intensity, self.n_peaks)
-            if len(spectrum.mz) == 0:
+            if len(spectrum.mz) < self.min_n_peaks:
                 raise ValueError
             spectrum.scale_intensity("root", 1)
             intensities = spectrum.intensity / np.linalg.norm(
@@ -163,7 +169,7 @@ def _process_peaks(
             return torch.tensor(np.array([spectrum.mz, intensities])).T.float()
         except ValueError:
             # Replace invalid spectra by a dummy spectrum.
-            return torch.tensor([[0, 1]]).float()
+            return None  # torch.tensor([[3, 3]]).float()
 
     @property
     def n_spectra(self) -> int:
@@ -194,6 +200,9 @@ class AnnotatedSpectrumDataset(SpectrumDataset):
     ----------
     annotated_spectrum_index : depthcharge.data.SpectrumIndex
         The MS/MS spectra to use as a dataset.
+    min_n_peaks : int
+        Minimum number of peaks allowed in each spectrum. Spectra with fewer
+        peaks are discarded.
     n_peaks : Optional[int]
         The number of top-n most intense peaks to keep in each spectrum. `None`
         retains all peaks.
@@ -216,6 +225,7 @@ class AnnotatedSpectrumDataset(SpectrumDataset):
     def __init__(
         self,
         annotated_spectrum_index: depthcharge.data.SpectrumIndex,
+        min_n_peaks: int = 20,
         n_peaks: int = 150,
         min_mz: float = 140.0,
         max_mz: float = 2500.0,
@@ -225,6 +235,7 @@ def __init__(
     ):
         super().__init__(
             annotated_spectrum_index,
+            min_n_peaks=min_n_peaks,
             n_peaks=n_peaks,
             min_mz=min_mz,
             max_mz=max_mz,
@@ -263,4 +274,5 @@ def __getitem__(self, idx: int) -> Tuple[torch.Tensor, float, int, str]:
         spectrum = self._process_peaks(
             mz_array, int_array, precursor_mz, precursor_charge
         )
-        return spectrum, precursor_mz, precursor_charge, peptide
+        if spectrum is not None:
+            return spectrum, precursor_mz, precursor_charge, peptide

casanovo/denovo/dataloaders.py

@@ -27,6 +27,9 @@ class DeNovoDataModule(pl.LightningDataModule):
         The batch size to use for training.
     eval_batch_size : int
         The batch size to use for inference.
+    min_n_peaks : int
+        Minimum number of peaks allowed in each spectrum. Spectra with fewer
+        peaks are discarded.
     n_peaks : Optional[int]
         The number of top-n most intense peaks to keep in each spectrum. `None`
         retains all peaks.
@@ -56,6 +59,7 @@ def __init__(
         test_index: Optional[AnnotatedSpectrumIndex] = None,
         train_batch_size: int = 128,
         eval_batch_size: int = 1028,
+        min_n_peaks: int = 20,
         n_peaks: Optional[int] = 150,
         min_mz: float = 50.0,
         max_mz: float = 2500.0,
@@ -70,6 +74,7 @@ def __init__(
         self.test_index = test_index
         self.train_batch_size = train_batch_size
         self.eval_batch_size = eval_batch_size
+        self.min_n_peaks = min_n_peaks
         self.n_peaks = n_peaks
         self.min_mz = min_mz
         self.max_mz = max_mz
@@ -97,6 +102,7 @@ def setup(self, stage: str = None, annotated: bool = True) -> None:
         if stage in (None, "fit", "validate"):
             make_dataset = functools.partial(
                 AnnotatedSpectrumDataset,
+                min_n_peaks=self.min_n_peaks,
                 n_peaks=self.n_peaks,
                 min_mz=self.min_mz,
                 max_mz=self.max_mz,
@@ -113,6 +119,7 @@ def setup(self, stage: str = None, annotated: bool = True) -> None:
         if stage in (None, "test"):
             make_dataset = functools.partial(
                 AnnotatedSpectrumDataset if annotated else SpectrumDataset,
+                min_n_peaks=self.min_n_peaks,
                 n_peaks=self.n_peaks,
                 min_mz=self.min_mz,
                 max_mz=self.max_mz,
@@ -201,6 +208,7 @@ def prepare_batch(
         The spectrum identifiers (during de novo sequencing) or peptide
         sequences (during training).
     """
+    batch = [spectrum for spectrum in batch if spectrum is not None]
     spectra, precursor_mzs, precursor_charges, spectrum_ids = list(zip(*batch))
     spectra = torch.nn.utils.rnn.pad_sequence(spectra, batch_first=True)
     precursor_mzs = torch.tensor(precursor_mzs)

casanovo/denovo/model_runner.py

@@ -292,6 +292,7 @@ def initialize_data_module(
             train_index=train_index,
             valid_index=valid_index,
             test_index=test_index,
+            min_n_peaks=self.config.min_n_peaks,
             min_mz=self.config.min_mz,
             max_mz=self.config.max_mz,
             min_intensity=self.config.min_intensity,