CROP

CROP removes redundant features from untargeted LC-HRMS metabolomics datasets by grouping highly correlated signals within defined retention time windows to reduce multiplicities and improve compound identification.

Key Features:

• LC-HRMS data compatibility: Operates on features derived from liquid chromatography–high-resolution mass spectrometry (LC-HRMS) datasets.
• Pearson pairwise correlations: Uses Pearson pairwise correlation coefficients to identify highly correlated features.
• Retention time windowing: Applies retention time constraints to group correlated features within defined RT windows.
• m/z-agnostic multiplicity reduction: Removes redundant features without requiring specific mass-to-charge (m/z) difference rules, accommodating unpredictable adducts and in-source fragments.
• Correlation network visualization: Produces graphical representations of correlation networks to inspect cluster composition and parameter effects.
• R-script implementation: Implemented as an open-source R script for computational processing of feature tables.

Scientific Applications:

• Multiplicity reduction in untargeted metabolomics: Reduces redundant signals to yield a condensed feature set for downstream analysis.
• Improved compound identification: Enhances accuracy of compound annotation by removing correlated adducts and fragments that confound identifications.
• Data quality enhancement and noise reduction: Lowers feature multiplicity and noise to improve the reliability of metabolomic datasets.
• Applications in systems biology, pharmacology, and environmental metabolomics: Supports analyses that require accurate feature lists for biological interpretation across these fields.

Methodology:

Computational steps compute Pearson pairwise correlations between features, apply retention time window criteria to group correlated features, remove grouped redundant features, and generate correlation network graphs; the approach is implemented as an R script.