rMSIcleanup

rMSIcleanup annotates and removes matrix-related spectral interferences in mass spectrometry imaging (MSI) data by analyzing chemical formulas and spatial distribution patterns, improving data quality in spatial metabolomics.

Key Features:

• Matrix Signal Annotation: Identifies matrix-related peaks using combined chemical formula evaluation and spatial distribution analysis to distinguish analyte ions from matrix-derived signals.
• Overlapping and Isobaric Peak Detection: Detects overlapping or isobaric peaks to prevent misclassification and improve annotation accuracy.
• AgLDI MSI Validation: Validated on silver-assisted laser desorption ionization (AgLDI) MSI datasets, including classification of silver cluster-related signals.

Scientific Applications:

• Spatial Metabolomics Data Processing: Enhances non-targeted MSI analyses by reducing matrix-derived noise and improving downstream analyses such as Principal Component Analysis (PCA).

Methodology:

rMSIcleanup integrates chemical formula matching with spatial distribution metrics to annotate matrix-derived peaks and applies algorithms for overlapping peak detection, enabling systematic removal of non-biological signals from MSI datasets.