metabCombiner

MetabCombiner is a computational pipeline designed to enhance liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomics data analysis. This tool addresses a common challenge in the field: the identification and alignment of compounds across datasets acquired under varying conditions. Traditional approaches struggle with this task due to significant shifts in chromatographic retention times, which complicate the direct comparison and combination of data from different experiments.

The core functionality of metabCombiner lies in its ability to accurately match known and unknown features across pairs of untargeted LC-MS datasets, subsequently concatenating these matches into a unified table of intersecting feature measurements. This process begins by grouping features based on their mass-to-charge (m/z) values, creating a structured search space for potential feature pair alignments. MetabCombiner then employs a spline fitting technique, which is applied to a set of selected retention time ordered pairs, enabling the adjustment of retention times across datasets.

The final step involves ranking possible alignments according to three criteria: m/z values, mapped retention times, and the similarity of relative abundances. This systematic approach allows metabCombiner to effectively bridge the gap between disparate datasets, facilitating collaborative compound identification efforts and the meta-analysis of combined datasets.