Network Marker Selection for Untargeted LC–MS Metabolomics Data

Abstract

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 (m/z) 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 http://web1.sph.emory.edu/users/tyu8/MSS.

Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics

“…In contrast to targeted metabolomics approaches, untargeted metabolomics aims to measure all detectable analytes in a sample, including unidentified metabolites [41]. Untargeted metabolomics is the most frequently used technique to elucidate the pathophysiological background and detect novel biomarkers in a broad range of diseases [147,148,149,150]. A forerunner of untargeted metabolomics was the use of gas chromatography-mass spectrometry (GC-MS) for urinary organic acids analysis and IEM diagnosis [151], used since the 1970s.…”

The metaRbolomics Toolbox in Bioconductor and beyond

“…Exploratory data analysis, univariate methods, hierarchical clustering (HCA), Principal Component Analysis (PCA) and Multi-Dimensional Scaling (MDS) like methods are very common in metabolite profiling approaches. Feature/variable selection is performed to find only the most significant metabolite candidates that explain the underlying research question, usually using univariate methods to target only specific metabolites that are interesting to the research question of the study [162,163,164,165].…”

New tools and resources in metabolomics: 2016–2017

“…MSS is a flexible network feature selection framework that combines metabolomics data with the genome‐scale metabolic network where the method adopts a sequential feature screening procedure and machine learning‐based criteria to select important subnetworks and identify the optimal feature matching simultaneously . The method identifies several subnetworks that are supported by the current literature as well as detects some subnetworks with plausible new functional implications.…”