Highly sensitive feature detection for high resolution LC/MS

TLDR

Liquid chromatography–mass spectrometry (LC/MS) is a key analytical tool for metabolomics, where accurate feature detection—identifying boundaries, centers, and intensities of two‑dimensional signals—is essential for complex samples such as plant extracts containing thousands of features. The study develops a new feature‑detection algorithm, centWave, for high‑resolution LC/MS data that collects regions of interest and applies continuous wavelet transformation with optional Gaussian fitting. centWave collects partial mass traces, applies continuous wavelet transformation and optional Gaussian fitting, and was evaluated on dilution series and plant extract mixtures to estimate recall, precision, and F‑score before being integrated into the Bioconductor XCMS package. The algorithm achieves the highest overall recall and precision, accurately detecting close‑by and partially overlapping features, thereby meeting the requirements of current metabolomics experiments and outperforming matchedFilter and centroidPicker.

Abstract

Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features – a reliable feature detection is mandatory. We developed a new feature detection algorithm centWave for high-resolution LC/MS data sets, which collects regions of interest (partial mass traces) in the raw-data, and applies continuous wavelet transformation and optionally Gauss-fitting in the chromatographic domain. We evaluated our feature detection algorithm on dilution series and mixtures of seed and leaf extracts, and estimated recall, precision and F-score of seed and leaf specific features in two experiments of different complexity. The new feature detection algorithm meets the requirements of current metabolomics experiments. centWave can detect close-by and partially overlapping features and has the highest overall recall and precision values compared to the other algorithms, matchedFilter (the original algorithm of XCMS) and the centroidPicker from MZmine. The centWave algorithm was integrated into the Bioconductor R-package XCMS and is available from http://www.bioconductor.org/