Mass spectrometry data preprocessing

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."