squallms

squallms implements a semi-labeled approach to untargeted mass spectrometry data that calculates peak quality metrics from raw mass-spec files and applies a logistic model to produce fully-labeled datasets for downstream analysis.

Key Features:

• Semi-labeled labeling approach: Labels MS features in bulk as high or low quality without requiring manual annotation of all features.
• Peak quality metrics calculation: Computes multiple metrics directly from raw mass-spec files to assess feature quality.
• Logistic model integration: Passes peak quality metrics to a simple logistic model to generate a fully-labeled dataset.

Scientific Applications:

• LC-MS data quality assurance: Enables speedy quality assessment of liquid chromatography-mass spectrometry data through automated feature labeling.
• Untargeted mass spectrometry analysis: Prepares raw untargeted MS data for downstream analysis by distinguishing high-quality from low-quality features.

Methodology:

The package pulls in raw data from mass-spec files, calculates peak quality metrics, labels MS features as high or low quality in bulk, and passes these metrics to a simple logistic model to produce a fully-labeled dataset.