massifquant

MassifQuant applies a Kalman filter and binless peak detection to correct calibration-induced gaps and improve peak quantitation in liquid chromatography-mass spectrometry (LC-MS) data for metabolomics and global molecular profiling.

Key Features:

• High Mass Accuracy: Addresses maintenance of high mass accuracy in LC-MS experiments affected by mass calibration systems that alternate between analyte samples and standard reference compounds.
• Kalman Filter-Based Gap Correction: Uses a Kalman filter to estimate and correct gaps caused by alternating mass calibration, recovering expected peak counts and intensity distributions.
• Binless Peak Picking and Quantitation: Performs peak detection and quantitation without predefined m/z binning to refine peak identification and intensity measurement.
• Integration with XCMS: Implemented within the XCMS software suite (available since version 1.23.3) to operate on LC-MS datasets processed in that framework.

Scientific Applications:

• Metabolomics: Enhances accurate detection and quantitation of metabolites for metabolomics studies.
• Global Molecular Profiling: Improves data integrity in global molecular profiling by correcting calibration-induced missing values.
• Biomarker Discovery and Pathway Analysis: Facilitates reliable identification and measurement of metabolites for metabolic pathway investigation and biomarker discovery.

Methodology:

Applies a Kalman filter for dynamic estimation and correction of missing data points caused by alternating mass calibration with standard reference compounds, combined with binless peak picking and quantitation to detect and quantify peaks without predefined bins.