gcms-processing

What problem does it solve?

GC-MS metabolomics data require specialized handling: raw EI spectra need smoothing, centroiding, deconvolution, retention index normalization, and robust library matching to produce confident compound annotations across samples and instruments.

Core Features & Use Cases

• Data import & inspection: load mzML/mzXML/CDF files and visualize TIC/EIC for quality control.
• Peak detection & deconvolution: optimized CentWave and centroiding workflows and export of deconvolved spectra for library searching.
• Retention index (RI) calculation: Kovats and linear RI functions to normalize retention times using alkane ladders.
• Library matching: RI-filtered spectral matching using matchms (cosine scoring) with configurable score and matched-peak thresholds for NIST/MSP and MassBank.
• Practical use case: process a batch of derivatized biological GC-MS samples to generate an annotated feature table suitable for downstream quantification and pathway analysis.

Quick Start

Process my GC-MS mzML file to detect peaks, calculate retention indices using the alkane ladder, deconvolve spectra, and match results against the NIST library to produce an annotated feature table.