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Abstract
<p class="first" id="P1">Untargeted metabolomics using high-resolution liquid chromatography–mass
spectrometry
(LC–MS) is becoming one of the major areas of high-throughput biology. Functional
analysis, that is, analyzing the data based on metabolic pathways or the genome-scale
metabolic network, is critical in feature selection and interpretation of metabolomics
data. One of the main challenges in the functional analyses is the lack of the feature
identity in the LC–MS data itself. By matching mass-to-charge ratio (
<i>m</i>/
<i>z</i>) values of the features to theoretical values derived from known metabolites,
some
features can be matched to one or more known metabolites. When multiple matchings
occur, in most cases only one of the matchings can be true. At the same time, some
known metabolites are missing in the measurements. Current network/pathway analysis
methods ignore the uncertainty in metabolite identification and the missing observations,
which could lead to errors in the selection of significant subnetworks/pathways. In
this paper, we propose a flexible network feature selection framework that combines
metabolomics data with the genome-scale metabolic network. The method adopts a sequential
feature screening procedure and machine learning-based criteria to select important
subnetworks and identify the optimal feature matching simultaneously. Simulation studies
show that the proposed method has a much higher sensitivity than the commonly used
maximal matching approach. For demonstration, we apply the method on a cohort of healthy
subjects to detect subnetworks associated with the body mass index (BMI). The method
identifies several subnetworks that are supported by the current literature, as well
as detects some subnetworks with plausible new functional implications. The R code
is available at
<a data-untrusted="" href="http://web1.sph.emory.edu/users/tyu8/MSS" id="d14823665e154"
target="xrefwindow">http://web1.sph.emory.edu/users/tyu8/MSS</a>.
</p><p id="P2">
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