apLCMS-adaptive processing of high-resolution LC/MS data

Motivation: Liquid chromatography-mass spectrometry (LC/MS) pro. ling is a promising approach for the quanti. cation of metabolites from complex biological samples. Significant challenges exist in the analysis of LC/MS data, including noise reduction, feature identification/quantification, feature alignment and computation efficiency. Result: Here we present a set of algorithms for the processing of high-resolution LC/MS data. The major technical improvements include the adaptive tolerance level searching rather than hard cutoff or binning, the use of non-parametric methods to. ne-tune intensity grouping, the use of run filter to better preserve weak signals and the model-based estimation of peak intensities for absolute quanti. cation. The algorithms are implemented in an R package apLCMS, which can efficiently process large LC/MS datasets.