HolobiomicsLab 
mass-spectrometry-data-preprocessing
Summary
Prepare raw or processed mass spectrometry spectra for deep learning inference by normalizing intensities and converting m/z–intensity pairs into vectorized feature representations. This skill bridges raw instrumental output and neural network input schemas, ensuring compatibility with downstream prediction systems like PS²MS.
When to use
You have raw mass spectra or processed feature matrices from liquid chromatography–mass spectrometry (LC-MS) or direct infusion MS that must be ingested by a deep learning model for substance identification. The input spectra are in a format native to MS instruments or databases but do not yet conform to the tokenization and normalization rules required by PS²MS or similar neural architectures.
When NOT to use
- Input is already a tokenized and normalized feature tensor matching the target model's input shape and value range.
- You are performing exploratory mass spectrometry data visualization or statistical summaries that do not require neural network inference.
Inputs
- raw mass spectra (m/z and intensity pairs)
- processed feature matrices from MS data
- mass spectrometry data in format compatible with PS²MS input schema
Outputs
- normalized and tokenized spectra tensors
- vectorized m/z–intensity feature matrices
- preprocessed data conforming to deep learning model input schema
How to apply
Load mass spectrometry input data as raw spectra (m/z–intensity pairs) or processed feature matrices. Apply PS²MS normalization and feature extraction by tokenizing and vectorizing m/z and intensity pairs according to the model's input schema. Ensure all spectra conform to identical feature dimensions and value ranges (e.g., intensity normalization to 0–1 or log-scale) before batching. Validate that preprocessed spectra match the expected input tensor shape and data type. Execute this preprocessing step immediately before invoking the pre-trained deep learning model's forward inference pass, as the model's performance depends critically on consistent input formatting.
Related tools
- PS²MS (Target deep learning prediction system that consumes preprocessed spectra to infer NPS class labels and confidence scores) — https://github.com/jhhung/PS2MS
- NEIMS (Generates predicted mass spectra for synthetic NPS database compounds; output spectra may require equivalent preprocessing)
- DeepEI (Predicts chemical fingerprints from mass spectra; interacts with preprocessed spectrum representations)
Evaluation signals
- Preprocessed spectra match the exact input tensor shape and data type required by PS²MS (confirm by inspecting tensor.shape and tensor.dtype).
- All intensity values fall within the normalized range (e.g., 0–1 or verified log-scale bounds) with no NaN or inf entries.
- m/z and intensity pairs are consistently tokenized across all spectra in the batch; verify by spot-checking multiple samples for uniform feature dimensionality.
- Forward inference on a small test batch completes without shape mismatch or dtype errors.
- Prediction confidence scores and class probability distributions are valid (sum to 1.0, no negative values).
Limitations
- Preprocessing rules are specific to the PS²MS model architecture; preprocessing for other deep learning MS systems (e.g., NEIMS or DeepEI) may differ in tokenization and normalization strategies.
- No publicly documented changelog or version history for preprocessing parameter changes; reproducibility across PS²MS versions may be affected.
- Preprocessing does not handle missing or corrupted spectra; input data quality assurance (e.g., intensity range validation, m/z monotonicity checks) must be performed upstream.
Evidence
- [other] Preprocess spectra according to PS2MS normalization and feature extraction requirements (tokenization/vectorization of m/z and intensity pairs).: "Preprocess spectra according to PS2MS normalization and feature extraction requirements (tokenization/vectorization of m/z and intensity pairs)."
- [other] Load mass spectrometry input data (raw spectra or processed feature matrices) compatible with PS2MS input schema.: "Load mass spectrometry input data (raw spectra or processed feature matrices) compatible with PS2MS input schema."
- [other] Execute forward inference pass on preprocessed spectra to generate NPS classification predictions and confidence scores.: "Execute forward inference pass on preprocessed spectra to generate NPS classification predictions and confidence scores."