2023 article
Metabolite Annotation Confidence Score (MACS): A Novel MSI Identification Scoring Tool
Eisenberg, S. M., Knizner, K. T., & Muddiman, D. C. (2023, August 22). JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY.
author keywords: IR-MALDESI; annotation scoring; mass measurementaccuracy; spectral accuracy; SSIM; MATLAB
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Source: Web Of Science
Added: September 5, 2023
Mass spectrometry imaging (MSI) is an analytical technique capable of measuring and visualizing the spatial distribution of thousands of ions across a sample. Measured ions can be putatively identified and annotated by comparing their mass-to-charge ratio (m/z) to a database of known compounds. For high-resolution, accurate mass (HRAM) imaging data sets, this is commonly performed by the annotation platform METASPACE. Annotations are reported with a metabolite-signal-match (MSM) score as a measure of the annotation's confidence level. However, the MSM scores reported by METASPACE often do not reflect a reasonable confidence level of an annotation and are not assigned consistently. The metabolite annotation confidence score (MACS) is an alternative scoring system based on fundamental mass spectrometry imaging metrics (mass measurement accuracy, spectral accuracy, and spatial distribution) to generate values that reflect the confidence of a specific annotation in HRAM-MSI data sets. Herein, the MACS system is characterized and compared to MSM scores from ions annotated by METASPACE.