Hybrid Feature Detection
and Information Accumulation
Using High-Resolution LC–MS Metabolomics Data
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Abstract
Feature detection is a critical step in the preprocessing
of liquid
chromatography–mass spectrometry (LC–MS) metabolomics
data. Currently, the predominant approach is to detect features using
noise filters and peak shape models based on the data at hand alone.
Databases of known metabolites and historical data contain information
that could help boost the sensitivity of feature detection, especially
for low-concentration metabolites. However, utilizing such information
in targeted feature detection may cause large number of false positives
because of the high levels of noise in LC–MS data. With high-resolution
mass spectrometry such as liquid chromatograph–Fourier transform
mass spectrometry (LC–FTMS), high-confidence matching of peaks
to known features is feasible. Here we describe a computational approach
that serves two purposes. First it boosts feature detection sensitivity
by using a hybrid procedure of both untargeted and targeted peak detection.
New algorithms are designed to reduce the chance of false-positives
by nonparametric local peak detection and filtering. Second, it can
accumulate information on the concentration variation of metabolites
over large number of samples, which can help find rare features and/or
features with uncommon concentration in future studies. Information
can be accumulated on features that are consistently found in real
data even before their identities are found. We demonstrate the value
of the approach in a proof-of-concept study. The method is implemented
as part of the R package apLCMS at http://www.sph.emory.edu/apLCMS/